⛏️ARCHEOLOGISTS⛏️ 💞EMPATHS💞 1

Author

Jenny Gutsell & other ARCHEOLOGISTS

Published

June 11, 2025

Introduction & setup

The preregistration for this document is at .

This is EMPATHS-1 (EMPATHS stands for EMPATHS: Machine-readable Publications to Analyze, Teach, Hypothesize, and Synthesize). EMPATHS-1 is an ARCHEOLOGISTS project (see https://archeologists.opens.science).

Here is the Codeberg repo for this project,here is the URL to the rendered version of this R Markdown file at Codeberg Pages, and here is the URL to the Open Science Framework project. The main Google Docs file for this project is here.

The extraction procedure is here, the PDF with the extraction instructions is located here, and the Rxs template is located here.

Background of this file’s structure

Note: this file was based on NITRO, the Narrated Illustration of a Transparent Review Outline, which accompanies the SysRevving book and the metabefor package. Throughout this file, links to the corresponding SysRevving chapters will be provided. For general reference, you may want to keep the SysRevving glossary ready.

Setup

Here we check for the required packages (without loading them into R’s search path with library() or require(), to safeguard against accidently forgetting to use the package::function() syntax), specify the paths, and set script-wide settings.

###-----------------------------------------------------------------------------
### Packages
###-----------------------------------------------------------------------------

if ((!(requireNamespace("metabefor", quietly = TRUE))) ||
      (packageVersion("metabefor") < "0.3")) {
  stop("You need to have at least version 0.3 of the `metabefor` package installed; ",
       "install it with:\n\ninstall.packages('metabefor');");
}

metabefor::checkPkgs(
  "here",               ### For easily access to files using 'relative paths'
  "preregr",            ### For specifying (pre)registrations
  "synthesisr",         ### For plotting
  "ggplot2"             ### For plotting
);

### Potentially update to the development version of some packages
# ufs::quietGitLabUpdate("r-packages/preregr@dev", quiet = FALSE);
# remotes::install_git("https://codeberg.org/R-packages/rock");
# remotes::install_git("https://codeberg.org/R-packages/metabefor");
# devtools::load_all("C:/git/R/metabefor");
# ufs::quietRemotesInstall("rmetaverse/synthesisr",
#                          func = "install_github", quiet = FALSE);

###-----------------------------------------------------------------------------
### Paths
###-----------------------------------------------------------------------------

basePath <- here::here();
preregPath <- file.path(basePath, "prereg");
scriptPath <- file.path(basePath, "scripts");
searchPath <- file.path(basePath, "search");
screeningPath <- file.path(basePath, "screening");
extractionPath <- file.path(basePath, "extraction");
rxsSpecPath <- file.path(basePath, "extraction-Rxs-spec");
outputPath <- file.path(basePath, "output");

###-----------------------------------------------------------------------------
### Settings
###-----------------------------------------------------------------------------

knitr::opts_chunk$set(
  echo = TRUE,
  comment = ""
);

###-----------------------------------------------------------------------------
### Extraction script Google sheets URL
###-----------------------------------------------------------------------------

rxsSpec_googleSheetsURL <-
  paste0("https://docs.google.com/spreadsheets/d/",
         "1hNu8IC1Y8bIXq-Bjgm5VFfNOiTEx1OunO5Cp3rmSO6g");

Planning

Research Question

(link to corresponding SysRevving chapter)

Example: The research question is whether the exponential explosion of the scientific literature is also reflected in a growing evidence base for health promotion interventions targeting recreational substance use.

Planning: Synthesis

(link to corresponding SysRevving chapter)

Example: To answer the research question, our synthesis will consist of a plot with years on the X axis, cumulative number of publications on the Y axis, and with spearately, differently colored lines for each substance.

Planning: Extraction

(link to corresponding SysRevving chapter)

Example: The R extraction script specification (Rxs spec) is stored in this Rxs spec google sheet. The below chunks load it, convert it into the Rxs template (that will then be copied and completed for each source from which data are extracted), and show these specifications.

R extraction script specification

Extractor instructions

metabefor::write_extractor_instructions(
  rxsSpecObject
);

Extractor instructions

Welcome!

Welcome to the extraction instructions for EMPATHS-1, the first systematic review in the EMPATHS project. If this is new to you, you may want to start at https://archeologists.opens.science/empaths.html. This PDF with extraction instructions is available from https://archeologists.opens.science/empaths-1/extractor-instructions.In this project, the focus is on construct definitions and measurement methods. Therefore, during extraction, these are the main entities that you will spend time on. In addition to the brief extraction instructions specified in these instructions and in the extraction script (.Rxs file), where you will register the extracted data, more extensive instructions are provided here.

Please start by reading these instructions carefully, as well as the extraction instructions at https://archeologists.opens.science/empaths-1/extraction. The instructions have two parts: this first part contains general instructions. The second part, starting from “Entity overview (list)”, contains entity-specific extraction instructions that will also be included in the Rxs Template (the “R Extraction Script Template”). That template is where you will conduct the extraction.

You will start any extraction by copying that template file to a new filename, and then opening that new file to enter the extracted information.

Naming the Rxs file

The filename should follow this format:

“name_year_sourceId_extractorId.rxs.rmd”

Where ‘name’ is the first word in the last (family) name of the first author, stripped of all characters other than a-z or A-Z; ‘year’ is the year of publication of the source; ‘sourceId’ is the source’s unique identifier (the ShortDOI if available; otherwise, the QURID); and ‘extractorId’ is the extractor’s unique identifier (i.e., your identifier).

General extraction instructions

To extract information from the sources, you scroll through the Rxs Template (that you just stored under a new name) and specify what you found for each entity.

You usually enter information by replacing the NULL with the entity content.

Usually, if something is not reported, replace the NULL with NA (also without quotes).

If you extract a number, you can usually just replace the NULL with that number. If you extract text, make sure to use double quotes around the text string.

Sometimes, you can extract multiple values (you can see this in the entity extraction instructions or in the instructions for the value template). In that case, you place them within a “concatenator” or “combiner”: c(). For example, c(1, 2, 3) for numbers, or c(“one”, “two”, “three”) for text strings.

Validating your extraction results

If you completed an R Extraction Script (.Rxs.Rmd file), you can (and should) immediately verify whether everything went well. There are two ways to do this. First, there’s the Extraction Validation App (EVA). Eva lives is at https://opens.science/apps/eva, and can validate your completed extraction script regardless of where you performed the extraction. Second, if you performed the extraction in RStudio, you can render the extraction script with CTRL-ALT-K. This will also produce the validation report, showing which entities validated and what is imported if your extraction script is parsed.

Extract construct definitions

You extract the empathy definition in a so-called “clustering entity” or “list entity”: a set of closely related entities placed closely together in the Rxs template. If you don’t use RStudio for extraction (and so do not benefit of syntax coloring), this can look a bit confusing; clustering entities contain the entity itself (often with default value NULL), immediately followed on the same line by a comment (starting with three hashes, “###”) with the entity’s description, extraction instruction, and the corresponding value template description. Because this is a lot of text, editors (such as Notepad++ and RStudio) will often apply soft word wrapping (splitting long lines and displaying them over multiple lines to prevent them from disappearing off the right side of the screen). You may want to study this clustering entity closely the first time.

The first entity in this clustering entity is the Empathy Construct Identifier for this specific definition. This is used to allows multiple empathy constructs to be defined in (and extracted from) the same source. However, you have to specify an Empathy Construct Identifier even if a source only has one definition: in other words, you always have to specify it. Remember: identifiers can only contains letters, digits, and underscores, and must start with a letter.

The second entity in this clustering entity is the empathy definition. When looking for a definition of empathy, start by using your source viewer (e.g. if the source is in PDF format, it may be your browser (e.g. Firefox) or a dedicated PDF viewer such as Sumatra or Adobe Acrobat) and use the search/find functionality to look for the text string “empathy” (assuming the source was written in English). Ignore definitions in the abstract. If the first occurrence of the construct name is accompanied by its definition, as the authors use it in their work, copy that definition into the extraction script. An example of a (very very brief) definition you might encounter is “Empathy is the ability to understand and relate to the emotions and experiences of others and to effectively communicate that understanding.” (note that definitions can also be much longer).

However, if the first occurrence is accompanied by a definition that the authors discuss, but not as a definition they use themselves but rather e.g. to introduce readers to the definitions that exist, move to the next occurrence. Similarly, if the first occurrence of the word is not accompanied by a definition at all, move to the next occurrence. For each occurrence, repeat this evaluation: are the authors defining what exactly empathy is? In other words, which parts of the human psyche they consider constituting empathy, and which they consider to reflect other constructs?

Once you extracted the first fragment (i.e. one or more sentences), repeat your search to see whether the authors provide additional aspects of their definition further on in the introduction. If they do, extract those as well. Extract fragments that occur at different places on the text as separate text elements (e.g. c("first bit", "second bit").

If the authors do not provide an explicit definition, then they may instead cite another source (e.g. an article or a book) and refer to the definition there as the one they use. In that case, obtain the shortdoi for that source, and extract that, in the full URL form (e.g. “https://doi.org/gf6btx”). This will enable us to later automatically identify all such URLs, and so categorize sources as either providing their own definition, providing no definition, or citing a definition from elsewhere in the literature (as well as compile a list of such references). If they cite a source that does not have a DOI, consult with the EMPATHS-1 coordinators, Jennifer Gutsell and/or Gjalt-Jorn Peters.

If the authors do not define empathy but also do not cite another source as providing the definition they use, extract NA to signify that the definition is missing from the source. Similarly, if authors are not explicit about their definition, extract NA. If authors only provide a definition of empathy in the abstract, report that in the comments field in this clustering entity.

If a source is written in a language that you do not understand, extract “lang” as construct definition. This will allow us to later try to find somebody who can read that language.

Finally, some sources may contain multiple empathy constructs. In that case, extract them into separate entities. To do this, copy the block starting with the line containing “START: empathyConstruct (REPEATING)” and ending with the line containing “END: empathyConstruct (REPEATING)”. Then complete both entities for the second empathy constructs, and repeat until you extracted all different empathy constructs in the source. (An example of such a paper is ns9s; see https://doi.org/ns9s for the PDF and [URL] for the completed Rxs file.)

Extracting a measurement or manipulation instrument

When extracting a measurement or manipulation (entities empathyMeasureId and empathyManipulationId), you specify their unique identifier. This identifier is taken from https://archeologists.opens.science/empathy-measures (from the column marked “identifier”). If the instrument you’re extracting is already in the list, you can just specify the relevant identifier in the extraction script.

However, if it does not yet exist, you have to add it. To do this, visit https://opens.science/apps/elsa, create an identifier, and add into the first column. Then specify the rest of the information as described in section “Specifying measurement instruments and/or manipulations” in https://archeologists.opens.science/extraction.

Just like definitions, a study can contain multiple measurement instruments or manipulation instruments. Again, copy the relevant block, from the line with “START: empathyMeasure (REPEATING)” to the line with “END: empathyMeasure (REPEATING)” for multiple measures, and from the line starting with “START: empathyManipulation (REPEATING)” to the line ending with “END: empathyManipulation (REPEATING)” for multiple manipulations.

Conversely, a study may not contain any measurement instruments or manipulations. In that case, you can specify “noMeasure” as value of empathyMeasureId and leave NULL as empathyMeasureConstructId, or “noManipulation” as empathyManipulationId and leave NULL as empathyManipulationConstructId.

Extracting multiple studies

Sometimes, a source reports on multiple studies. If the studies use different measurement instruments or manipulation instruments, copy the study block like you may have copied definition blocks, measurement instrument blocks, or manipulation instrument blocks before. However, study blocks are larger, and themselves contain ‘repeating’ container entities (specifically, the measurement instrument blocks and the manipulation instrument blocks are specified within the respective study).

To copy the study block, copy the lines in between the line with “START: singleStudyContainer (REPEATING)” to the line with “END: singleStudyContainer (REPEATING)”. As you’ll see, this is quite a large part of the Rxs file. Also note that you may have to specify the population for each study separately.

How to create an identifier

To create a unique identifier for a TOM, TOQ, or TOI, you can either use the R package {psyverse} or the Elsa app. To use Elsa, visit https://opens.science/apps/elsa. Identifiers follow the following format. They start with a brief lowercase sequence of letters that is often an acronym or abbreviation of the instrument’s name (e.g. ‘iri’, ‘bespt’, and ‘epitome’). This is followed by a number: the number of items in the measurement instrument; 0 for a manipulation; or 00 for continuous measurement such as EEG. That is followed by the language of the measurement instrument in ISO 639-3 code (see the extraction instructions for extracting the language a source was written in). That is followed by an underscore, and then the last identifier bit as produced by Elsa.

Future reference

In the future, we will specify the extracted measurement instruments and manipulations in an open repository. For that stage a number of instructions are included here. During the extraction phase of this project, you can ignore this; this is simply retained here for future reference.

If it is a questionnaire, you can choose to specificy it as a TOQ (“Tabulated Open Questionnaire”) specification, enabling importing it into the questionnaire repository at https://operationalizations.com. This is not yet possible for measurement instruments that do not consist of questions and for manipulations; those have to be specified as TOM (“Tabulated Open Metadata”) specifications. Depending on what you choose, follow the corresponding set of instructions below.

Minimal specification of a measurement or manipulation instrument

To specify a TOM (“Tabulated Open Metadata”) specification, you need to complete these steps:

visit https://archeologists.opens.science/empathy-tabulated-specs
open “TOM-spec—bespt0eng_7rtpjgf3”
save a copy under a different name but in the same folder.
create an identifier prefix (see the procedure below for details) and enter it in cell B3
visit https://opens.science/apps/elsa, enter the prefix, and create an identifier
enter the result in cell B4 as UMID
complete the other fields
open the spreadsheet at https://archeologists.opens.science/empathy-measures again and add a row with the UMID you just created

Full specification of a questionnaire

To specify a TOQ (“Tabulated Open Questionnaire”) specification, you need to complete these steps:

visit https://archeologists.opens.science/empathy-tabulated-specs
open “TOQ-spec—eq60eng_7rs8g3bd”
save a copy under a different name but in the same folder.
create an identifier prefix (see the procedure below for details) and enter it in cell B3
visit https://opens.science/apps/elsa, enter the prefix, and create an identifier
enter the result in cell B4 as UQID
complete the other fields
open the spreadsheet at https://archeologists.opens.science/empathy-measures again and add a row with the UQID you just created

How to create an identifier

Entity overview (list)

This is an overview of the entities to extract, their titles and descriptions, and other details that will become part of the extraction script template that will be used for the actual extraction.

General

General information

Type: Entity Container
Identifier: general
Path in extraction script tree: source > general
Repeating: FALSE

QURID

Quasi Unique Identifier Record Identifier (QURID).

Extraction instructions: This is already available in the tracking sheet; a QURID was added to every record. We will use this to automatically import bibliographic information available in that file, such as title, keywords, potentially abstract, etc.

Type: Extractable Entity
Identifier: qurid
Value description: A single character value that is used as an identifier and so is always mandatory and can only contain a-z, A-Z, 0-9, and underscores, and must start with a letter.
Path in extraction script tree: source > general > qurid
Value template: string_identifier
Repeating: FALSE

Language

The language in which the article is written as ISO 639-3 code (e.g., to list the 10 most spoken languages: “eng” for English, “zho” for Chinese, “hin” for Hindi, “spa” for Spanish, “fra” for French, and “ara” for Arabic, “ben” for Bengali, “por” for Portuguese, “rus” for Russian, and “urd” for Urdu).

Extraction instructions: Use ISO 639-3 to extract this (see https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes and https://en.wikipedia.org/wiki/ISO_639-3).

Type: Extractable Entity
Identifier: language
Value description: A single character value
Path in extraction script tree: source > general > language
Value template: string
Repeating: FALSE

Empirical

Whether this source reports on one or more empirical studies (i.e. studies where data were created in the context of the study that authors report on, for example through experimentation, observation, simulation, or similar means).

Extraction instructions: Extract “yes” if this source reports results from at least one empirical study. Extract “no” if it does not report results from an empirical study. Extract “unclear” if you are not sure whether results from an empirical study are reported.

Note that since the species under study can be synthetic, collecting data from generative AI or a simulation also counts as empirical.

Type: Extractable Entity
Identifier: empirical
Value description: A string that has to exactly match one of the values specified in the “values” column of the Coding sheet, and that can be omitted (i.e. is allowed to be NULL).
Path in extraction script tree: source > general > empirical
Value template: categorical_omittable
Repeating: FALSE

Empathy Constructs

This container entity is used to extract information about the various empathy constructs studied in this source.

Type: Entity Container
Identifier: empathyConstructs
Path in extraction script tree: source > empathyConstructs
Repeating: FALSE

Empathy Construct

This clustering entity contains information about one single empathy construct as defined in this source. Note that we take a broad view of empathy constructs; this also includes empathy not as a part of the human psyche, but as it may be perceived to be expressed in, for example, a text or recording.

Type: Extractable Entity List
Identifier: empathyConstruct

Empathy Construct Identifier	This is a unique identifier for this empathy construct. It can be used elsewhere in this extraction script to refer to this construct (for example when extracting measurement instruments or manipulations).
Empathy Definition	The definition of empathy the authors use.
Empathy Definition Confidence	How confident you are that the definition you extracted is indeed how the authors defined empathy in this source.
Empathy Construct Type	The type of empathy construct: psychological construct or not.
Empathy Definition Notes	Any notes you want to specify.

Path in extraction script tree: source > empathyConstructs > empathyConstruct
Repeating: TRUE

Methods

This container entity holds entities related to the methods used by the study.

Type: Entity Container
Identifier: methods
Path in extraction script tree: source > methods
Repeating: FALSE

Reported Studies

This contained entity holds the studies reported on in this source.

Type: Entity Container
Identifier: reportedStudies
Path in extraction script tree: source > reportedStudies
Repeating: FALSE

Single Study

This container entity contains information about a single study. This is important because some sources report on multiple studies.

Type: Entity Container
Identifier: singleStudyContainer
Path in extraction script tree: source > reportedStudies > singleStudyContainer
Repeating: TRUE

Population

Information about the population of this study.

Type: Entity Container
Identifier: population
Path in extraction script tree: source > reportedStudies > singleStudyContainer > population
Repeating: FALSE

Species

Whether the sample was drawn from humans or non-human populations

Extraction instructions: Extract “human” if the sample description in the methods section indicates a human sample. Extract “animal” if the description in the methods section of none of the studies reported indicates a human sample. Extract “synthetic” if the data were produced by an automated algorithm (e.g. a simulation such as a large language model or an agent-based model). If another species was studies, extract “other” and then also specify that species in the “population_species_other” entity. If the collected data was produced by multiple species, extract all species as a vector (see the examples).

Type: Extractable Entity
Identifier: population_species
Value description: A vector of strings where each element has to exactly match one of the values specified in the “values” column of the Coding sheet
Path in extraction script tree: source > reportedStudies > singleStudyContainer > population > population_species
Value template: categorical_multi
Repeating: FALSE

Other Species

If the species that was specified was “other”, then as this entity extract the text fragment where the authors describe the species they studied.

Extraction instructions: Extract the literal text the authors use; if the species was not extracted as “other”, extract this as NA.

Type: Extractable Entity
Identifier: population_species_other
Value description: A single character value; can be NA or even NULL
Path in extraction script tree: source > reportedStudies > singleStudyContainer > population > population_species_other
Value template: string_omittable
Repeating: FALSE

Manipulation

Whether the source involves a manipulation of empathy (or intervention, behavior change method, therapy component, etc).

Extraction instructions: Assess whether the source introduces or involves a procedure designed to increase, decrease, or otherwise alter the research units’ empathy (i.e. the humans or animals that are studied). This can be called a manipulation in experimental psychology, a behavior change method, technique or principle in behavior change science, or a therapy component in clinical psychology. Other terms are also possible of course: the key is whether the procedure or stimulus was designed to influence empathy. If you conclude that such a procedure or stimulus is described in the source as one of the focal topics, extract “yes”. If you conclude that no such procedure or stimulus is described, extract “no”. If it is unclear whether that is the case, extract “unclear”. If nothing is reported that allows you to draw any conclusions, extract NA (without quotes).

Type: Extractable Entity
Identifier: involvesManipulation
Value description: A string that has to exactly match one of the values specified in the “values” column of the Coding sheet, and that can be omitted (i.e. is allowed to be NULL).
Path in extraction script tree: source > reportedStudies > singleStudyContainer > involvesManipulation
Value template: categorical_omittable
Repeating: FALSE

Empathy Measures

This container entity holds entities specifying how empathy was measured.

Type: Entity Container
Identifier: empathyMeasures
Path in extraction script tree: source > reportedStudies > singleStudyContainer > empathyMeasures
Repeating: FALSE

Empathy Measure

Container entity for this empathy measure.

Type: Extractable Entity List
Identifier: empathyMeasure

Empathy Measure Identifier	The identifier for the empathy measure that was used to measure empathy in this study in this source.
Measured Construct	The identifier of the construct as entered in its extracted definition above.

Path in extraction script tree: source > reportedStudies > singleStudyContainer > empathyMeasures > empathyMeasure
Repeating: TRUE

Empathy Manipulations

This container entity holds entities specifying how empathy was manipulated.

Type: Entity Container
Identifier: empathyManipulations
Path in extraction script tree: source > reportedStudies > singleStudyContainer > empathyManipulations
Repeating: FALSE

Empathy Manipulation

Container entity for this empathy manipulation.

Type: Extractable Entity List
Identifier: empathyManipulation

Empathy Manipulation Identifier	The identifier for the empathy manipulation that was used to manipulate empathy in this study in this source.
Manipulated Construct	The identifier of the construct as entered in its extracted definition above.

Path in extraction script tree: source > reportedStudies > singleStudyContainer > empathyManipulations > empathyManipulation
Repeating: TRUE

extractorInstructions <-
  metabefor::write_extractor_instructions(
    rxsSpecObject,
    outputFile = file.path(
        extractionPath,
        "extractor-instructions.pdf"
      )
  );

#install.packages('openalexR');

# works_from_orcids <- openalexR::oa_fetch(
#   entity = "works",
#   author.orcid = "0000-0002-0336-9589",
#   verbose = TRUE
# )

updateQuery <- FALSE;

if (updateQuery) {
  
  sources <- openalexR::oa_fetch(
    display_name.search = "empathy",
    publication_year = "2023",
    type = "types/article",
    primary_topic.field.id = "fields/32",
    open_access.is_oa = "true",
    output = "list",
    verbose = TRUE
  );

  created_dates <-
    unlist(lapply(sources, function(source) { return(source$created_date) }));
  
  ### Convert all sources in the first query execution to a data frame
  sourcesDf <-
    openalexR::works2df(sources[created_dates < "2024-11-14"]);
  
  ### Sanity check
  
  if (any(duplicated(sourcesDf$id))) {
    stop("Duplicated source identifiers!");
  }
  
  ### Attach QURIDs; specify origin to ensure replicability
  
  sourcesDf$QURID <-
    metabefor::generate_qurids(
      nrow(sourcesDf),
      origin = as.POSIXct("2024-11-14 15:54:14 CET")
    );
  
  
  ### Prepare author field for further processing
  #metabefor::vecTxt(sourcesDf$author[[1]]$au_display_name)
  
  sourcesDf$authorString <-
    unlist(lapply(sourcesDf$author,
                  function(x) {
                    if (is.data.frame(x)) {
                      return(metabefor::vecTxt(x$au_display_name));
                    } else if (is.na(x)) {
                      return("No author specified");
                    } else {
                      browser()
                    }
                  }));
  
  sourcesDf$firstAuthorLastName <-
    trimws(tolower(unlist(lapply(
      sourcesDf$author,
      function(x) {
        if (is.data.frame(x)) {
          return(gsub(".* ", "", x[1, 'au_display_name']));
        } else if (is.na(x)) {
          return("NA");
        } else {
          browser();
        }
      }
    ))));
  
  ### Correct an erroneous DOI
  sourcesDf$doi[sourcesDf$doi == "10.1145/3570945.xxxxxxx"] <-
    "10.1145/3570945"
  
  ### Get ShortDOIs
  sourcesDf$shortDOI <-
    metabefor::get_short_dois(
      sourcesDf$doi, silent=FALSE, progress=TRUE
    );
  
  ### Set source identifiers
  sourcesDf$sourceId <-
    ifelse(
      is.na(sourcesDf$shortDOI),
      sourcesDf$QURID,
      sourcesDf$shortDOI
    );
  
  ### Set filenames
  sourcesDf$filename <-
    paste0(sourcesDf$firstAuthorLastName, "_",
           sourcesDf$publication_year, "_",
           sourcesDf$sourceId);
  
  ### Add fields for extraction
  sourcesDf$extractorId <- "";
  sourcesDf$extractionStatus <- "";

  saveRDS(sources, file.path(searchPath, "empaths-1-query-1---sources.rds"));
  saveRDS(sourcesDf, file.path(searchPath, "empaths-1-query-1---sourcesDf.rds"));
  
} else {

  sources <-
    readRDS(file.path(searchPath, "empaths-1-query-1---sources.rds"));
  sourcesDf <-
    readRDS(file.path(searchPath, "empaths-1-query-1---sourcesDf.rds"));
  
}

extractionTracking <-
  as.data.frame(
    sourcesDf[
      ,
      c(
        "title",
        "authorString",
        "sourceId",
        "extractorId",
        "extractionStatus",
        "filename",
        "QURID",
        "publication_year",
        "id"
      )
    ]
  );

wb <-
  openxlsx::createWorkbook();

openxlsx::addWorksheet(wb, "extractionTracking");

openxlsx::writeData(
  wb,
  sheet = "extractionTracking",
  x = extractionTracking
);

### Select URL to find source
sourcesDf$sourceURL <-
  ifelse(
    is.na(sourcesDf$pdf_url),
    sourcesDf$doi,
    sourcesDf$pdf_url
  );

openxlsx::writeFormula(
  wb,
  "extractionTracking",
  x =
    paste0(
      'HYPERLINK("', sourcesDf$sourceURL, '", "', sourcesDf$title, '")'
    ),
  startCol = 1,
  startRow = 2
);

openxlsx::writeFormula(
  wb,
  "extractionTracking",
  x =
    paste0(
      'HYPERLINK("', sourcesDf$id, '", "', sourcesDf$id, '")'
    ),
  startCol = 9,
  startRow = 2
);

### Set column widths
openxlsx::setColWidths(
  wb, "extractionTracking",
  cols = 1:9,
  widths = c(50, 30, 10, 10, 10, 20, 12, 10, 30)
);

openxlsx::saveWorkbook(
  wb,
  file.path(extractionPath, "autogenerated---EMPATHS-1---extraction-phase-1---2024-11-14.xlsx"),
  overwrite = TRUE
);

Basic Rxs tree structure

metabefor::show_rxsTree_in_rxsStructure(
  rxsSpecObject,
  output = file.path(outputPath, "extraction-tree.pdf")
);

                                            levelName
1  source                                            
2   ¦--general                                       
3   ¦   ¦--qurid                                     
4   ¦   ¦--language                                  
5   ¦   °--empirical                                 
6   ¦--empathyConstructs                             
7   ¦   °--empathyConstruct                          
8   ¦       ¦--empathyConstructId                    
9   ¦       ¦--empathyConstructDefinition            
10  ¦       ¦--empathyConstructConfidence            
11  ¦       ¦--empathyConstructType                  
12  ¦       °--empathyConstructNotes                 
13  ¦--methods                                       
14  °--reportedStudies                               
15      °--singleStudyContainer                      
16          ¦--population                            
17          ¦   ¦--population_species                
18          ¦   °--population_species_other          
19          ¦--involvesManipulation                  
20          ¦--empathyMeasures                       
21          ¦   °--empathyMeasure                    
22          ¦       ¦--empathyMeasureId              
23          ¦       °--empathyMeasureConstructId     
24          °--empathyManipulations                  
25              °--empathyManipulation               
26                  ¦--empathyManipulationId         
27                  °--empathyManipulationConstructId

Extraction instructions

cat(rxsSpecObject$rxsInstructions);

Extractor instructions

Welcome!

You will start any extraction by copying that template file to a new filename, and then opening that new file to enter the extracted information.

Naming the Rxs file

The filename should follow this format:

“name_year_sourceId_extractorId.rxs.rmd”

General extraction instructions

To extract information from the sources, you scroll through the Rxs Template (that you just stored under a new name) and specify what you found for each entity.

You usually enter information by replacing the NULL with the entity content.

Usually, if something is not reported, replace the NULL with NA (also without quotes).

If you extract a number, you can usually just replace the NULL with that number. If you extract text, make sure to use double quotes around the text string.

Validating your extraction results

Extract construct definitions

If a source is written in a language that you do not understand, extract “lang” as construct definition. This will allow us to later try to find somebody who can read that language.

Extracting a measurement or manipulation instrument

Extracting multiple studies

How to create an identifier

Future reference

Minimal specification of a measurement or manipulation instrument

To specify a TOM (“Tabulated Open Metadata”) specification, you need to complete these steps:

visit https://archeologists.opens.science/empathy-tabulated-specs
open “TOM-spec—bespt0eng_7rtpjgf3”
save a copy under a different name but in the same folder.
create an identifier prefix (see the procedure below for details) and enter it in cell B3
visit https://opens.science/apps/elsa, enter the prefix, and create an identifier
enter the result in cell B4 as UMID
complete the other fields
open the spreadsheet at https://archeologists.opens.science/empathy-measures again and add a row with the UMID you just created

Full specification of a questionnaire

To specify a TOQ (“Tabulated Open Questionnaire”) specification, you need to complete these steps:

visit https://archeologists.opens.science/empathy-tabulated-specs
open “TOQ-spec—eq60eng_7rs8g3bd”
save a copy under a different name but in the same folder.
create an identifier prefix (see the procedure below for details) and enter it in cell B3
visit https://opens.science/apps/elsa, enter the prefix, and create an identifier
enter the result in cell B4 as UQID
complete the other fields
open the spreadsheet at https://archeologists.opens.science/empathy-measures again and add a row with the UQID you just created

How to create an identifier

Entity overview

cat(rxsSpecObject$entityOverview_list);

Entity overview (list)

This is an overview of the entities to extract, their titles and descriptions, and other details that will become part of the extraction script template that will be used for the actual extraction.

General

General information

Type: Entity Container
Identifier: general
Path in extraction script tree: source > general
Repeating: FALSE

QURID

Quasi Unique Identifier Record Identifier (QURID).

Language

Extraction instructions: Use ISO 639-3 to extract this (see https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes and https://en.wikipedia.org/wiki/ISO_639-3).

Empirical

Note that since the species under study can be synthetic, collecting data from generative AI or a simulation also counts as empirical.

Empathy Constructs

This container entity is used to extract information about the various empathy constructs studied in this source.

Type: Entity Container
Identifier: empathyConstructs
Path in extraction script tree: source > empathyConstructs
Repeating: FALSE

Empathy Construct

Type: Extractable Entity List
Identifier: empathyConstruct

Empathy Construct Identifier	This is a unique identifier for this empathy construct. It can be used elsewhere in this extraction script to refer to this construct (for example when extracting measurement instruments or manipulations).
Empathy Definition	The definition of empathy the authors use.
Empathy Definition Confidence	How confident you are that the definition you extracted is indeed how the authors defined empathy in this source.
Empathy Construct Type	The type of empathy construct: psychological construct or not.
Empathy Definition Notes	Any notes you want to specify.

Path in extraction script tree: source > empathyConstructs > empathyConstruct
Repeating: TRUE

Methods

This container entity holds entities related to the methods used by the study.

Type: Entity Container
Identifier: methods
Path in extraction script tree: source > methods
Repeating: FALSE

Reported Studies

This contained entity holds the studies reported on in this source.

Type: Entity Container
Identifier: reportedStudies
Path in extraction script tree: source > reportedStudies
Repeating: FALSE

Single Study

This container entity contains information about a single study. This is important because some sources report on multiple studies.

Type: Entity Container
Identifier: singleStudyContainer
Path in extraction script tree: source > reportedStudies > singleStudyContainer
Repeating: TRUE

Population

Information about the population of this study.

Type: Entity Container
Identifier: population
Path in extraction script tree: source > reportedStudies > singleStudyContainer > population
Repeating: FALSE

Species

Whether the sample was drawn from humans or non-human populations

Other Species

If the species that was specified was “other”, then as this entity extract the text fragment where the authors describe the species they studied.

Extraction instructions: Extract the literal text the authors use; if the species was not extracted as “other”, extract this as NA.

Manipulation

Whether the source involves a manipulation of empathy (or intervention, behavior change method, therapy component, etc).

Empathy Measures

This container entity holds entities specifying how empathy was measured.

Type: Entity Container
Identifier: empathyMeasures
Path in extraction script tree: source > reportedStudies > singleStudyContainer > empathyMeasures
Repeating: FALSE

Empathy Measure

Container entity for this empathy measure.

Type: Extractable Entity List
Identifier: empathyMeasure

Empathy Measure Identifier	The identifier for the empathy measure that was used to measure empathy in this study in this source.
Measured Construct	The identifier of the construct as entered in its extracted definition above.

Path in extraction script tree: source > reportedStudies > singleStudyContainer > empathyMeasures > empathyMeasure
Repeating: TRUE

Empathy Manipulations

This container entity holds entities specifying how empathy was manipulated.

Type: Entity Container
Identifier: empathyManipulations
Path in extraction script tree: source > reportedStudies > singleStudyContainer > empathyManipulations
Repeating: FALSE

Empathy Manipulation

Container entity for this empathy manipulation.

Type: Extractable Entity List
Identifier: empathyManipulation

Empathy Manipulation Identifier	The identifier for the empathy manipulation that was used to manipulate empathy in this study in this source.
Manipulated Construct	The identifier of the construct as entered in its extracted definition above.

Path in extraction script tree: source > reportedStudies > singleStudyContainer > empathyManipulations > empathyManipulation
Repeating: TRUE

Extraction script template

This is the extraction script generated based on the extraction script specification.

cat("\n\n<pre><textarea rows='40' cols='124' style='font-family:monospace;font-size:11px;white-space:pre;'>",
    unlist(rxsSpecObject$rxsTemplate),
    "</textarea></pre>\n\n",
    sep="\n");

```{r rxs-setup-chunk-oXjhN8, echo=FALSE, results='hide'}
```


<!--~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-->
<!--                                                                       -->
<!-- Welcome to the R Extraction Script (.rxs.Rmd file) for this source!   -->
<!-- This version of the Rxs template was produced at 2025-06-11.          -->
<!--                                                                       -->
<!-- You can now start extracting. If you haven't yet studied the          -->
<!-- extractor instructions, please do so first. They are located at       -->
<!-- https://archeologists.opens.science/empaths-1/extractor-instructions  -->
<!--                                                                       -->
<!-- If you're all set, good luck! 💪                                       -->
<!--                                                                       -->
<!--~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-->


```{r rxs-extraction-chunk, echo=FALSE}
##############################################################################
############################################ START: uniqueSourceIdentifier ###
##############################################################################
uniqueSourceIdentifier <- 
##############################################################################
### 
### SET UNIQUE SOURCE IDENTIFIER
### 
### A unique identifier used in this systematic review to refer to this
### source
### 
##############################################################################
    
    NULL
    
##############################################################################
########################################### VALUE DESCRIPTION AND EXAMPLES ###
##############################################################################
### 
### A unique identifier to use in this systematic review. For sources
### with a DOI, this is the last part of the shortDOI as looked up
### through https://shortdoi.org (the part after the "10/"). For sources
### without a DOI (and so, without a shortDOI), this can be, for example,
### the QURID (Quasi-Unique Record Identifier) that was designated during
### the screening phase or which you can create with
### `metabefor::qurid()`.
### 
### EXAMPLES:
### 
### "g5fj"
### "qurid_7h4pksl6"
### 
##############################################################################
############################################## END: uniqueSourceIdentifier ###
##############################################################################

##############################################################################
############################################### START: extractorIdentifier ###
##############################################################################
extractorIdentifier <- 
##############################################################################
### 
### SPECIFY YOUR EXTRACTOR IDENTIFIER
### 
### An identifier unique to every extractor
### 
##############################################################################
    
    NULL
    
##############################################################################
########################################### VALUE DESCRIPTION AND EXAMPLES ###
##############################################################################
### 
### Identifiers can only consist of (lower or uppercase) Latin letters
### [a-zA-Z], Arabic numerals [0-9], and underscores [_], and always have
### to start with a letter.
### 
### EXAMPLES:
### 
### "extractor_1"
### "Alex"
### 
##############################################################################
################################################# END: extractorIdentifier ###
##############################################################################


##############################################################################
##################################################### START: source (ROOT) ###
##############################################################################
rxsObject <- data.tree::Node$new('source');
currentEntity <- rxsObject;
##############################################################################

  ############################################################################
  ######################################################### START: general ###
  ############################################################################
  currentEntity <- currentEntity$AddChild('general');
  ############################################################################
  ### 
  ### GENERAL
  ### 
  ### General information
  ### 
  ############################################################################
      
  
    ##########################################################################
    ######################################################### START: qurid ###
    ##########################################################################
    currentEntity <- currentEntity$AddChild('qurid');
    currentEntity[['value']] <-
    ##########################################################################
    ### 
    ### QURID
    ### 
    ### Quasi Unique Identifier Record Identifier (QURID).
    ### 
    ### This is already available in the tracking sheet; a QURID was
    ### added to every record. We will use this to automatically
    ### import bibliographic information available in that file, such
    ### as title, keywords, potentially abstract, etc.
    ### 
    ##########################################################################
        
        NULL
        
    ##########################################################################
    ####################################### VALUE DESCRIPTION AND EXAMPLES ###
    ##########################################################################
    ### 
    ### A single character value that is used as an identifier and so
    ### is always mandatory and can only contain a-z, A-Z, 0-9, and
    ### underscores, and must start with a letter.
    ### 
    ### EXAMPLES:
    ### 
    ### "example1"
    ### "another_identifier_example"
    ### "finalExample"
    ### 
    ##########################################################################
    currentEntity[['validation']] <- expression((length(VALUE)==1) && grepl("[a-zA-Z][a-zA-Z0-9_]*", VALUE));
    currentEntity <- currentEntity$parent;
    ##########################################################################
    ########################################################### END: qurid ###
    ##########################################################################
  
  
    ##########################################################################
    ###################################################### START: language ###
    ##########################################################################
    currentEntity <- currentEntity$AddChild('language');
    currentEntity[['value']] <-
    ##########################################################################
    ### 
    ### LANGUAGE
    ### 
    ### The language in which the article is written as ISO 639-3
    ### code (e.g., to list the 10 most spoken languages: "eng" for
    ### English, "zho" for Chinese, "hin" for Hindi, "spa" for
    ### Spanish, "fra" for French, and "ara" for Arabic, "ben" for
    ### Bengali, "por" for Portuguese, "rus" for Russian, and "urd"
    ### for Urdu).
    ### 
    ### Use ISO 639-3 to extract this (see
    ### https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes
    ### and https://en.wikipedia.org/wiki/ISO_639-3).
    ### 
    ##########################################################################
        
        NULL
        
    ##########################################################################
    ####################################### VALUE DESCRIPTION AND EXAMPLES ###
    ##########################################################################
    ### 
    ### A single character value
    ### 
    ### EXAMPLES:
    ### 
    ### "eng"
    ### "spa"
    ### "zho"
    ### "ara"
    ### 
    ##########################################################################
    currentEntity[['validation']] <- expression(all(is.na(VALUE)) || (is.character(VALUE) && length(VALUE) == 1));
    currentEntity <- currentEntity$parent;
    ##########################################################################
    ######################################################## END: language ###
    ##########################################################################
  
  
    ##########################################################################
    ##################################################### START: empirical ###
    ##########################################################################
    currentEntity <- currentEntity$AddChild('empirical');
    currentEntity[['value']] <-
    ##########################################################################
    ### 
    ### EMPIRICAL
    ### 
    ### Whether this source reports on one or more empirical studies
    ### (i.e. studies where data were created in the context of the
    ### study that authors report on, for example through
    ### experimentation, observation, simulation, or similar means).
    ### 
    ### Extract "yes" if this source reports results from at least
    ### one empirical study. Extract "no" if it does not report
    ### results from an empirical study. Extract "unclear" if you are
    ### not sure whether results from an empirical study are
    ### reported.
    ### 
    ### Note that since the species under study can be synthetic,
    ### collecting data from generative AI or a simulation also
    ### counts as empirical.
    ### 
    ##########################################################################
        
        NULL
        
    ##########################################################################
    ####################################### VALUE DESCRIPTION AND EXAMPLES ###
    ##########################################################################
    ### 
    ### A string that has to exactly match one of the values
    ### specified in the "values" column of the Coding sheet, and
    ### that can be omitted (i.e. is allowed to be NULL).
    ### 
    ### EXAMPLE:
    ### 
    ### c("yes", "no", "unclear")
    ### 
    ##########################################################################
    currentEntity[['validation']] <- expression(is.null(VALUE) || all(is.na(VALUE)) || (VALUE %in% c("yes", "no", "unclear")));
    currentEntity <- currentEntity$parent;
    ##########################################################################
    ####################################################### END: empirical ###
    ##########################################################################
  
      
  ############################################################################
  ############################################################################
  currentEntity <- currentEntity$parent;
  ############################################################################
  ########################################################### END: general ###
  ############################################################################


  ############################################################################
  ############################################### START: empathyConstructs ###
  ############################################################################
  currentEntity <- currentEntity$AddChild('empathyConstructs');
  ############################################################################
  ### 
  ### EMPATHY CONSTRUCTS
  ### 
  ### This container entity is used to extract information about the
  ### various empathy constructs studied in this source.
  ### 
  ############################################################################
      
  
    ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    ##########################################################################
    ################################## START: empathyConstruct (REPEATING) ###
    ##########################################################################
    currentEntity <- currentEntity$AddChild('empathyConstruct__1__');
    currentEntity[['value']] <-
    ##########################################################################
    ### 
    ### EMPATHY CONSTRUCT
    ### 
    ### This clustering entity contains information about one single
    ### empathy construct as defined in this source. Note that we
    ### take a broad view of empathy constructs; this also includes
    ### empathy not as a part of the human psyche, but as it may be
    ### perceived to be expressed in, for example, a text or
    ### recording.
    ### 
    ##########################################################################
        
        list(empathyConstructId = NULL,          ### Empathy Construct Identifier: This is a unique identifier for this empathy construct. It can be used elsewhere in this extraction script to refer to this construct (for example when extracting measurement instruments or manipulations). Empathy construct identifiers have to be unique in this source and are ideally sufficiently descriptive to be somewhat self-explanatory (e.g. "empathyDef_1"). Identifiers always have to start with a Latin letter (a-z or A-Z), followed by one or more Latin letters, Arabic digits (0-9) and underscores. [Examples: "example1"; "another_identifier_example"; "finalExample"] [Value description: A single character value that is used as an identifier and so is always mandatory and can only contain a-z, A-Z, 0-9, and underscores, and must start with a letter.]
             empathyConstructDefinition = NULL,  ### Empathy Definition: The definition of empathy the authors use. The instruction for extracting empathy definitions is provided in the general extraction instructions; please refer to that section. [Examples: c("first value", "second value")] [Value description: A character vector (i.e. one or more strings)]
             empathyConstructConfidence = NULL,  ### Empathy Definition Confidence: How confident you are that the definition you extracted is indeed how the authors defined empathy in this source. If you are absolutely not confident, specify 0; if you are very (maximally) confident, specify 3; if you are slightly confident, specify 1; if you are quite confident, specify 2. Note that if you want, you can specify notes in the Empathy Definition Notes entity, the last entity in this clustering (list) entity. [Examples: 30; 8762] [Value description: Any valid whole number (cannot be "NA")]
             empathyConstructType = NULL,        ### Empathy Construct Type: The type of empathy construct: psychological construct or not. Here, indicate whether the empathy construct as defined here represents a part of the human psyche (as assumed, hypothesized, or theorized by the source's authors; or, if that is not stated, what you infer from the source's title, domain, and text) or not. If this empathy construct is a part of the human psyche, extract "yes". If not, extract "no". If you cannot determine whether it is, extract "unclear". [Examples: c("yes", "no", "unclear")] [Value description: A string that has to exactly match one of the values specified in the "values" column of the Coding sheet]
             empathyConstructNotes = NULL);      ### Empathy Definition Notes: Any notes you want to specify. Here, there is room to add any notes you have about this construct definition or the process of extracting it. [Examples: "example"; "another example"] [Value description: A single character value; can be NA or even NULL]
        
    ##########################################################################
    currentEntity[['validation']] <- list(`empathyConstructId` = expression((length(VALUE)==1) && grepl("[a-zA-Z][a-zA-Z0-9_]*", VALUE)),
                                          `empathyConstructDefinition` = expression(all(is.na(VALUE)) || (is.character(VALUE))),
                                          `empathyConstructConfidence` = expression((is.numeric(VALUE) && (VALUE%%1==0) && (length(VALUE) == 1))),
                                          `empathyConstructType` = expression((VALUE %in% c("yes", "no", "unclear"))),
                                          `empathyConstructNotes` = expression(is.null(VALUE) || all(is.na(VALUE)) || (is.character(VALUE) && length(VALUE) == 1)));
    currentEntity$name <- metabefor::nodeName(currentEntity$value[[1]], "empathyConstruct__1__", node=currentEntity);
    currentEntity <- currentEntity$parent;
    ##########################################################################
    #################################### END: empathyConstruct (REPEATING) ###
    ##########################################################################
    ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
      
  ############################################################################
  ############################################################################
  currentEntity <- currentEntity$parent;
  ############################################################################
  ################################################# END: empathyConstructs ###
  ############################################################################


  ############################################################################
  ######################################################### START: methods ###
  ############################################################################
  currentEntity <- currentEntity$AddChild('methods');
  ############################################################################
  ### 
  ### METHODS
  ### 
  ### This container entity holds entities related to the methods used
  ### by the study.
  ### 
  ############################################################################
      
      
  ############################################################################
  ############################################################################
  currentEntity <- currentEntity$parent;
  ############################################################################
  ########################################################### END: methods ###
  ############################################################################


  ############################################################################
  ################################################# START: reportedStudies ###
  ############################################################################
  currentEntity <- currentEntity$AddChild('reportedStudies');
  ############################################################################
  ### 
  ### REPORTED STUDIES
  ### 
  ### This contained entity holds the studies reported on in this
  ### source.
  ### 
  ############################################################################
      
  
    ##########################################################################
    ############################## START: singleStudyContainer (REPEATING) ###
    ##########################################################################
    currentEntity <- currentEntity$AddChild('singleStudyContainer__1__');
    ##########################################################################
    ### 
    ### SINGLE STUDY
    ### 
    ### This container entity contains information about a single
    ### study. This is important because some sources report on
    ### multiple studies.
    ### 
    ##########################################################################
        
    
      ########################################################################
      ################################################## START: population ###
      ########################################################################
      currentEntity <- currentEntity$AddChild('population');
      ########################################################################
      ### 
      ### POPULATION
      ### 
      ### Information about the population of this study.
      ### 
      ########################################################################
          
      
        ######################################################################
        ######################################## START: population_species ###
        ######################################################################
        currentEntity <- currentEntity$AddChild('population_species');
        currentEntity[['value']] <-
        ######################################################################
        ### 
        ### SPECIES
        ### 
        ### Whether the sample was drawn from humans or non-human
        ### populations
        ### 
        ### Extract "human" if the sample description in the
        ### methods section indicates a human sample. Extract
        ### "animal" if the description in the methods section of
        ### none of the studies reported indicates a human
        ### sample. Extract "synthetic" if the data were produced
        ### by an automated algorithm (e.g. a simulation such as
        ### a large language model or an agent-based model). If
        ### another species was studies, extract "other" and then
        ### also specify that species in the
        ### "population_species_other" entity. If the collected
        ### data was produced by multiple species, extract all
        ### species as a vector (see the examples).
        ### 
        ######################################################################
            
            NULL
            
        ######################################################################
        ################################### VALUE DESCRIPTION AND EXAMPLES ###
        ######################################################################
        ### 
        ### A vector of strings where each element has to exactly
        ### match one of the values specified in the "values"
        ### column of the Coding sheet
        ### 
        ### EXAMPLES:
        ### 
        ### "human"
        ### "unclear"
        ### "other"
        ### c("animal", "synthetic")
        ### 
        ######################################################################
        currentEntity[['validation']] <- expression(all(is.na(VALUE)) || all(VALUE %in% c("human", "animal", "synthetic", "unclear", "other")));
        currentEntity <- currentEntity$parent;
        ######################################################################
        ########################################## END: population_species ###
        ######################################################################
      
      
        ######################################################################
        ################################## START: population_species_other ###
        ######################################################################
        currentEntity <- currentEntity$AddChild('population_species_other');
        currentEntity[['value']] <-
        ######################################################################
        ### 
        ### OTHER SPECIES
        ### 
        ### If the species that was specified was "other", then
        ### as this entity extract the text fragment where the
        ### authors describe the species they studied.
        ### 
        ### Extract the literal text the authors use; if the
        ### species was not extracted as "other", extract this as
        ### NA.
        ### 
        ######################################################################
            
            NA
            
        ######################################################################
        ################################### VALUE DESCRIPTION AND EXAMPLES ###
        ######################################################################
        ### 
        ### A single character value; can be NA or even NULL
        ### 
        ### EXAMPLES:
        ### 
        ### "example"
        ### "another example"
        ### 
        ######################################################################
        currentEntity[['validation']] <- expression(is.null(VALUE) || all(is.na(VALUE)) || (is.character(VALUE) && length(VALUE) == 1));
        currentEntity <- currentEntity$parent;
        ######################################################################
        #################################### END: population_species_other ###
        ######################################################################
      
          
      ########################################################################
      ########################################################################
      currentEntity <- currentEntity$parent;
      ########################################################################
      #################################################### END: population ###
      ########################################################################
    
    
      ########################################################################
      ######################################## START: involvesManipulation ###
      ########################################################################
      currentEntity <- currentEntity$AddChild('involvesManipulation');
      currentEntity[['value']] <-
      ########################################################################
      ### 
      ### MANIPULATION
      ### 
      ### Whether the source involves a manipulation of empathy (or
      ### intervention, behavior change method, therapy component,
      ### etc).
      ### 
      ### Assess whether the source introduces or involves a
      ### procedure designed to increase, decrease, or otherwise
      ### alter the research units' empathy (i.e. the humans or
      ### animals that are studied). This can be called a
      ### manipulation in experimental psychology, a behavior
      ### change method, technique or principle in behavior change
      ### science, or a therapy component in clinical psychology.
      ### Other terms are also possible of course: the key is
      ### whether the procedure or stimulus was designed to
      ### influence empathy. If you conclude that such a procedure
      ### or stimulus is described in the source as one of the
      ### focal topics, extract "yes". If you conclude that no such
      ### procedure or stimulus is described, extract "no". If it
      ### is unclear whether that is the case, extract "unclear".
      ### If nothing is reported that allows you to draw any
      ### conclusions, extract NA (without quotes).
      ### 
      ########################################################################
          
          NULL
          
      ########################################################################
      ##################################### VALUE DESCRIPTION AND EXAMPLES ###
      ########################################################################
      ### 
      ### A string that has to exactly match one of the values
      ### specified in the "values" column of the Coding sheet, and
      ### that can be omitted (i.e. is allowed to be NULL).
      ### 
      ### EXAMPLE:
      ### 
      ### c("yes", "no", "unclear")
      ### 
      ########################################################################
      currentEntity[['validation']] <- expression(is.null(VALUE) || all(is.na(VALUE)) || (VALUE %in% c("yes", "no", "unclear")));
      currentEntity <- currentEntity$parent;
      ########################################################################
      ########################################## END: involvesManipulation ###
      ########################################################################
    
    
      ########################################################################
      ############################################# START: empathyMeasures ###
      ########################################################################
      currentEntity <- currentEntity$AddChild('empathyMeasures');
      ########################################################################
      ### 
      ### EMPATHY MEASURES
      ### 
      ### This container entity holds entities specifying how
      ### empathy was measured.
      ### 
      ########################################################################
          
      
        ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        ######################################################################
        ################################ START: empathyMeasure (REPEATING) ###
        ######################################################################
        currentEntity <- currentEntity$AddChild('empathyMeasure__1__');
        currentEntity[['value']] <-
        ######################################################################
        ### 
        ### EMPATHY MEASURE
        ### 
        ### Container entity for this empathy measure.
        ### 
        ######################################################################
            
            list(empathyMeasureId = NULL,            ### Empathy Measure Identifier: The identifier for the empathy measure that was used to measure empathy in this study in this source. The instruction for extracting measurement instruments and locating or producing the unique measure identifier is provided in the general extraction instructions; please refer to that section. [Examples: "example1"; c("id1", "id2")] [Value description: One or multiple values that are used as an identifier to an external thing and can only contain a-z, A-Z, 0-9, and underscores, and must start with a letter; can also be NA or NULL.]
                 empathyMeasureConstructId = NULL);  ### Measured Construct: The identifier of the construct as entered in its extracted definition above. Here, copy-paste the construct's identifier as extracted above. [Examples: <no example>] [Value description: A string that specifies another entity (can be missing, i.e. NA, or even omitted, i.e. NULL)]
            
        ######################################################################
        currentEntity[['validation']] <- list(`empathyMeasureId` = expression(is.null(VALUE) || all(is.na(VALUE)) || all(grepl("[a-zA-Z][a-zA-Z0-9_]*", VALUE))),
                                              `empathyMeasureConstructId` = expression(is.null(VALUE) || all(is.na(VALUE)) || (!is.na(VALUE) && rxs_findEntity(node, "empathyConstructId", VALUE))));
        currentEntity$name <- metabefor::nodeName(currentEntity$value[[1]], "empathyMeasure__1__", node=currentEntity);
        currentEntity[['entityRefs']] <- c(empathyMeasureConstructId="empathyConstructId");
        currentEntity <- currentEntity$parent;
        ######################################################################
        ################################## END: empathyMeasure (REPEATING) ###
        ######################################################################
        ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
      
          
      ########################################################################
      ########################################################################
      currentEntity <- currentEntity$parent;
      ########################################################################
      ############################################### END: empathyMeasures ###
      ########################################################################
    
    
      ########################################################################
      ######################################## START: empathyManipulations ###
      ########################################################################
      currentEntity <- currentEntity$AddChild('empathyManipulations');
      ########################################################################
      ### 
      ### EMPATHY MANIPULATIONS
      ### 
      ### This container entity holds entities specifying how
      ### empathy was manipulated.
      ### 
      ########################################################################
          
      
        ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        ######################################################################
        ########################### START: empathyManipulation (REPEATING) ###
        ######################################################################
        currentEntity <- currentEntity$AddChild('empathyManipulation__1__');
        currentEntity[['value']] <-
        ######################################################################
        ### 
        ### EMPATHY MANIPULATION
        ### 
        ### Container entity for this empathy manipulation.
        ### 
        ######################################################################
            
            list(empathyManipulationId = NULL,            ### Empathy Manipulation Identifier: The identifier for the empathy manipulation that was used to manipulate empathy in this study in this source. The instruction for extracting manipulations and locating or producing the unique manipulation identifier is provided in the general extraction instructions; please refer to that section. [Examples: "example1"; c("id1", "id2")] [Value description: One or multiple values that are used as an identifier to an external thing and can only contain a-z, A-Z, 0-9, and underscores, and must start with a letter; can also be NA or NULL.]
                 empathyManipulationConstructId = NULL);  ### Manipulated Construct: The identifier of the construct as entered in its extracted definition above. Here, copy-paste the construct's identifier as extracted above. [Examples: <no example>] [Value description: A string that specifies another entity (can be missing, i.e. NA, or even omitted, i.e. NULL)]
            
        ######################################################################
        currentEntity[['validation']] <- list(`empathyManipulationId` = expression(is.null(VALUE) || all(is.na(VALUE)) || all(grepl("[a-zA-Z][a-zA-Z0-9_]*", VALUE))),
                                              `empathyManipulationConstructId` = expression(is.null(VALUE) || all(is.na(VALUE)) || (!is.na(VALUE) && rxs_findEntity(node, "empathyConstructId", VALUE))));
        currentEntity$name <- metabefor::nodeName(currentEntity$value[[1]], "empathyManipulation__1__", node=currentEntity);
        currentEntity[['entityRefs']] <- c(empathyManipulationConstructId="empathyConstructId");
        currentEntity <- currentEntity$parent;
        ######################################################################
        ############################# END: empathyManipulation (REPEATING) ###
        ######################################################################
        ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
      
          
      ########################################################################
      ########################################################################
      currentEntity <- currentEntity$parent;
      ########################################################################
      ########################################## END: empathyManipulations ###
      ########################################################################
    
        
    ##########################################################################
    ##########################################################################
    currentEntity <- currentEntity$parent;
    ##########################################################################
    ################################ END: singleStudyContainer (REPEATING) ###
    ##########################################################################
  
      
  ############################################################################
  ############################################################################
  currentEntity <- currentEntity$parent;
  ############################################################################
  ################################################### END: reportedStudies ###
  ############################################################################

    
##############################################################################
####################################################### END: source (ROOT) ###
##############################################################################
class(rxsObject) <- c('rxs', 'rxsObject', class(rxsObject));
rxsObject$rxsMetadata <- list(rxsVersion='0.3.0', moduleId=NULL, id=uniqueSourceIdentifier, extractorId=extractorIdentifier);
##############################################################################
##############################################################################
##############################################################################
```


<!--~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-->
<!--                                                                       -->
<!-- Well done! You are now done extracting this source. Great job!!!      -->
<!--                                                                       -->
<!-- Now, please knit the R Extraction Script into an HTML file and        -->
<!-- carefully check whether you entered everything correctly, since it    -->
<!-- will cost much less time to correct any errors, now that you still    -->
<!-- have this source in your mind, than later on when you'll have to      -->
<!-- dive into it all over.                                                -->
<!--                                                                       -->
<!--~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-->


```{r rxs-template-specification-chunk-NBbR0X, echo=FALSE, results='hide'}
```

```{r rxs-validation-chunk-MGxFqf, results='asis'}
metabefor::heading('Validation results', headingLevel = 1);
metabefor::rxs_validation(
  rxsObject,
  stopOnFailure = FALSE,
  rxsTemplateSpec = rxsTemplateSpec);
cat(paste0('- ', rxsObject$validationLog), sep='\n');
```

```ichalbotebbmitllzpmjfwcguzwocywsswodytij
if ((!(exists('parsingMultipleRxsFiles'))) || (!parsingMultipleRxsFiles)) {  metabefor::rxs_partial(rxsObject); }
```

```{r rxs-setup-chunk-oXjhN8, include=FALSE, messages=FALSE}
### First check for (and perhaps install) metabefor
if (!(requireNamespace('metabefor', quietly = TRUE))) {
  install.packages('metabefor', repos='http://cran.rstudio.com');
}

### Settings
knitr::opts_chunk$set(echo = FALSE);     ### Suppress R command printing
knitr::opts_chunk$set(comment = NA);     ### Suppress output prefix
```

---
title: "`r paste0('Rxs for \'', uniqueSourceIdentifier, '\' (', metabefor::knittedFileSansExt(), ')')`"
author: "`r paste0('Extractor: ', extractorIdentifier)`"
date: "`r format(Sys.time(), '%Y-%m-%d at %H:%M:%S %Z (UTC%z)')`"
output:
  html_document:
    embed-resources: true
    toc: false
params:
  rxsVersion: "0.3.0"
  rxsRootName: "source"
  rxsObjectName: "rxsObject"
editor_options:
  chunk_output_type: console
---



<!-- Here, the original Rxs template specification is included. ~~~~~~~~~~~-->


```{r rxs-template-specification-chunk-NBbR0X, echo=FALSE}

rxsTemplateSpec <- list();


rxsTemplateSpec$entities <-

  structure(list(title = c("General", "QURID", "Language", "Empathy Constructs", 
  "Empathy Construct", "Empathy Construct Identifier", "Empathy Definition", 
  "Empathy Definition Confidence", "Empathy Construct Type", "Empathy Definition Notes", 
  "Empirical", "Methods", "Reported Studies", "Single Study", "Population", 
  "Species", "Other Species", "Manipulation", "Empathy Measures", 
  "Empathy Measure", "Empathy Measure Identifier", "Measured Construct", 
  "Empathy Manipulations", "Empathy Manipulation", "Empathy Manipulation Identifier", 
  "Manipulated Construct"), description = c("General information", 
  "Quasi Unique Identifier Record Identifier (QURID).", "The language in which the article is written as ISO 639-3 code (e.g., to list the 10 most spoken languages: \"eng\" for English, \"zho\" for Chinese, \"hin\" for Hindi, \"spa\" for Spanish, \"fra\" for French, and \"ara\" for Arabic, \"ben\" for Bengali, \"por\" for Portuguese, \"rus\" for Russian, and \"urd\" for Urdu).", 
  "This container entity is used to extract information about the various empathy constructs studied in this source.", 
  "This clustering entity contains information about one single empathy construct as defined in this source. Note that we take a broad view of empathy constructs; this also includes empathy not as a part of the human psyche, but as it may be perceived to be expressed in, for example, a text or recording.", 
  "This is a unique identifier for this empathy construct. It can be used elsewhere in this extraction script to refer to this construct (for example when extracting measurement instruments or manipulations).", 
  "The definition of empathy the authors use.", "How confident you are that the definition you extracted is indeed how the authors defined empathy in this source.", 
  "The type of empathy construct: psychological construct or not.", 
  "Any notes you want to specify.", "Whether this source reports on one or more empirical studies (i.e. studies where data were created in the context of the study that authors report on, for example through experimentation, observation, simulation, or similar means).", 
  "This container entity holds entities related to the methods used by the study.", 
  "This contained entity holds the studies reported on in this source.", 
  "This container entity contains information about a single study. This is important because some sources report on multiple studies.", 
  "Information about the population of this study.", "Whether the sample was drawn from humans or non-human populations", 
  "If the species that was specified was \"other\", then as this entity extract the text fragment where the authors describe the species they studied.", 
  "Whether the source involves a manipulation of empathy (or intervention, behavior change method, therapy component, etc).", 
  "This container entity holds entities specifying how empathy was measured.", 
  "Container entity for this empathy measure.", "The identifier for the empathy measure that was used to measure empathy in this study in this source.", 
  "The identifier of the construct as entered in its extracted definition above.", 
  "This container entity holds entities specifying how empathy was manipulated.", 
  "Container entity for this empathy manipulation.", "The identifier for the empathy manipulation that was used to manipulate empathy in this study in this source.", 
  "The identifier of the construct as entered in its extracted definition above."
  ), instructions = c(NA, "This is already available in the tracking sheet; a QURID was added to every record. We will use this to automatically import bibliographic information available in that file, such as title, keywords, potentially abstract, etc.", 
  "Use ISO 639-3 to extract this (see https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes and https://en.wikipedia.org/wiki/ISO_639-3).", 
  NA, NA, "Empathy construct identifiers have to be unique in this source and are ideally sufficiently descriptive to be somewhat self-explanatory (e.g. \"empathyDef_1\"). Identifiers always have to start with a Latin letter (a-z or A-Z), followed by one or more Latin letters, Arabic digits (0-9) and underscores.", 
  "The instruction for extracting empathy definitions is provided in the general extraction instructions; please refer to that section.", 
  "If you are absolutely not confident, specify 0; if you are very (maximally) confident, specify 3; if you are slightly confident, specify 1; if you are quite confident, specify 2. Note that if you want, you can specify notes in the Empathy Definition Notes entity, the last entity in this clustering (list) entity.", 
  "Here, indicate whether the empathy construct as defined here represents a part of the human psyche (as assumed, hypothesized, or theorized by the source's authors; or, if that is not stated, what you infer from the source's title, domain, and text) or not. If this empathy construct is a part of the human psyche, extract \"yes\". If not, extract \"no\". If you cannot determine whether it is, extract \"unclear\".", 
  "Here, there is room to add any notes you have about this construct definition or the process of extracting it.", 
  "Extract \"yes\" if this source reports results from at least one empirical study. Extract \"no\" if it does not report results from an empirical study. Extract \"unclear\" if you are not sure whether results from an empirical study are reported.\n\nNote that since the species under study can be synthetic, collecting data from generative AI or a simulation also counts as empirical.", 
  NA, NA, NA, NA, "Extract \"human\" if the sample description in the methods section indicates a human sample. Extract \"animal\" if the description in the methods section of none of the studies reported indicates a human sample. Extract \"synthetic\" if the data were produced by an automated algorithm (e.g. a simulation such as a large language model or an agent-based model). If another species was studies, extract \"other\" and then also specify that species in the \"population_species_other\" entity. If the collected data was produced by multiple species, extract all species as a vector (see the examples).", 
  "Extract the literal text the authors use; if the species was not extracted as \"other\", extract this as NA.", 
  "Assess whether the source introduces or involves a procedure designed to increase, decrease, or otherwise alter the research units' empathy (i.e. the humans or animals that are studied). This can be called a manipulation in experimental psychology, a behavior change method, technique or principle in behavior change science, or a therapy component in clinical psychology. Other terms are also possible of course: the key is whether the procedure or stimulus was designed to influence empathy. If you conclude that such a procedure or stimulus is described in the source as one of the focal topics, extract \"yes\". If you conclude that no such procedure or stimulus is described, extract \"no\". If it is unclear whether that is the case, extract \"unclear\". If nothing is reported that allows you to draw any conclusions, extract NA (without quotes).", 
  NA, NA, "The instruction for extracting measurement instruments and locating or producing the unique measure identifier is provided in the general extraction instructions; please refer to that section.", 
  "Here, copy-paste the construct's identifier as extracted above.", 
  NA, NA, "The instruction for extracting manipulations and locating or producing the unique manipulation identifier is provided in the general extraction instructions; please refer to that section.", 
  "Here, copy-paste the construct's identifier as extracted above."
  ), identifier = c("general", "qurid", "language", "empathyConstructs", 
  "empathyConstruct", "empathyConstructId", "empathyConstructDefinition", 
  "empathyConstructConfidence", "empathyConstructType", "empathyConstructNotes", 
  "empirical", "methods", "reportedStudies", "singleStudyContainer", 
  "population", "population_species", "population_species_other", 
  "involvesManipulation", "empathyMeasures", "empathyMeasure", 
  "empathyMeasureId", "empathyMeasureConstructId", "empathyManipulations", 
  "empathyManipulation", "empathyManipulationId", "empathyManipulationConstructId"
  ), valueTemplate = c(NA, "string_identifier", "string", NA, NA, 
  "string_identifier", "string_multi", "integer_mandatory", "categorical_mandatory", 
  "string_omittable", "categorical_omittable", NA, NA, NA, NA, 
  "categorical_multi", "string_omittable", "categorical_omittable", 
  NA, NA, "externalIdentifier_omittable", "string_entityRef_omittable", 
  NA, NA, "externalIdentifier_omittable", "string_entityRef_omittable"
  ), parent = c(NA, "general", "general", NA, "empathyConstructs", 
  "empathyConstruct", "empathyConstruct", "empathyConstruct", "empathyConstruct", 
  "empathyConstruct", "general", NA, NA, "reportedStudies", "singleStudyContainer", 
  "population", "population", "singleStudyContainer", "singleStudyContainer", 
  "empathyMeasures", "empathyMeasure", "empathyMeasure", "singleStudyContainer", 
  "empathyManipulations", "empathyManipulation", "empathyManipulation"
  ), ontologyId = c(NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_), validValues = c(NA, 
  NA, NA, NA, NA, NA, NA, NA, "\"yes\" || \"no\" || \"unclear\"", 
  NA, "\"yes\" || \"no\" || \"unclear\"", NA, NA, NA, NA, "\"human\" || \"animal\" || \"synthetic\" || \"unclear\" || \"other\"", 
  NA, "\"yes\" || \"no\" || \"unclear\"", NA, NA, NA, NA, NA, NA, 
  NA, NA), default = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
  NA, NA, NA, NA, NA, NA, "NA", NA, NA, NA, NA, NA, NA, NA, NA, 
  NA), examples = c(NA, NA, "\"eng\" || \"spa\" || \"zho\" || \"ara\"", 
  NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "\"human\" || \"unclear\" || \"other\" || c(\"animal\", \"synthetic\")", 
  NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), module = c(NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_
  ), entityRef = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
  NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "empathyConstructId", 
  NA, NA, NA, "empathyConstructId"), fieldRef = c(NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_
  ), owner = c(NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_), list = c(NA, NA, NA, NA, "TRUE", 
  NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "TRUE", 
  NA, NA, NA, "TRUE", NA, NA), collapsing = c(NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
  NA_character_, NA_character_, NA_character_, NA_character_), 
      repeating = c(NA, NA, NA, NA, "TRUE", NA, NA, NA, NA, NA, 
      NA, NA, NA, "TRUE", NA, NA, NA, NA, NA, "TRUE", NA, NA, NA, 
      "TRUE", NA, NA), recurring = c(NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_
      ), recursing = c(NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_), identifying = c(NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_, NA_character_, NA_character_, NA_character_, 
      NA_character_)), row.names = c(NA, -26L), class = "data.frame")



rxsTemplateSpec$valueTemplates <-

  structure(list(identifier = c("numeric", "numeric_multi", "integer", 
  "integer_mandatory", "integer_multi", "integer_length4_multi", 
  "integer_length4", "string", "string_omittable", "string_mandatory", 
  "string_multi", "string_multi_mandatory", "string_entityRef_mandatory", 
  "string_entityRef_optional", "string_fieldRef_optional", "string_entityRef_omittable", 
  "string_fieldRef_omittable", "countrycode", "countrycode_mandatory", 
  "categorical", "categorical_omittable", "categorical_mandatory", 
  "categorical_multi", "generalPresence", "matrix_crosstab", "string_identifier", 
  "externalIdentifier", "externalIdentifier_omittable", "ORCID", 
  "ROR"), description = c("Any valid number", "A vector of valid numbers", 
  "Any valid whole number", "Any valid whole number (cannot be \"NA\")", 
  "A vector of integers (i.e. one or more whole numbers)", "A numeric vector of years, where each year must have exactly 4 digits.", 
  "A numeric single value denoting a year, which must have exactly 4 digits.", 
  "A single character value", "A single character value; can be NA or even NULL", 
  "A single character value that cannot be omitted", "A character vector (i.e. one or more strings)", 
  "A character vector (i.e. one or more strings)", "A string that specifies another entity and which MUST be provided", 
  "A string that specifies another entity (can be missing, i.e. NA)", 
  "A string that specifies another field in another entity (can be missing, i.e. NA).", 
  "A string that specifies another entity (can be missing, i.e. NA, or even omitted, i.e. NULL)", 
  "A string that specifies another field in another entity (can be missing, i.e. NA, or even omitted, i.e. NULL)", 
  "A character vector of the ISO 3166-1 alpha-2 country code(s)", 
  "A character vector of the ISO 3166-1 alpha-2 country code(s)", 
  "A string that has to exactly match one of the values specified in the \"values\" column of the Coding sheet", 
  "A string that has to exactly match one of the values specified in the \"values\" column of the Coding sheet, and that can be omitted (i.e. is allowed to be NULL).", 
  "A string that has to exactly match one of the values specified in the \"values\" column of the Coding sheet", 
  "A vector of strings where each element has to exactly match one of the values specified in the \"values\" column of the Coding sheet", 
  "Whether the thing being coded was present or not.", "A table with frequencies; variable 1 in columns, variable 2 in rows; always work from absence/negative/less (left, top) to presence/positive/more (right, bottom)", 
  "A single character value that is used as an identifier and so is always mandatory and can only contain a-z, A-Z, 0-9, and underscores, and must start with a letter.", 
  "One or multiple values that are used as an identifier to an external thing and can only contain a-z, A-Z, 0-9, and underscores, and must start with a letter.", 
  "One or multiple values that are used as an identifier to an external thing and can only contain a-z, A-Z, 0-9, and underscores, and must start with a letter; can also be NA or NULL.", 
  "An ORCID. Only include the ORCID itself; if the article lists the ORCID as an URL, omit the part before the last slash (see the example).", 
  "A ROR identifier"), validValues = c(NA, NA, NA, NA, NA, NA, 
  NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
  NA, "\"Present\" || \"Absent\"", NA, NA, NA, NA, NA, NA), default = c("NULL", 
  "NULL", "NULL", "NULL", "NULL", "NULL", "NULL", "NULL", "NULL", 
  "NULL", "NULL", "NULL", "NULL", "NULL", "NULL", "NULL", "NULL", 
  "NULL", "NULL", "NULL", "NULL", "NULL", "NULL", "NULL", "NULL", 
  "NULL", "NULL", "NULL", "NULL", NA), examples = c("2.3 || 643.2", 
  "c(23.43, 62) || 52.2 || c(76, 12.56, 42)", "30 || 8762", "30 || 8762", 
  "c(23, 62) || 52 || c(76, 12, 42)", "2001 || c(2001, 2002)", 
  "2001 || 2023", "\"example\" || \"another example\"", "\"example\" || \"another example\"", 
  "\"example\" || \"another example\"", "c(\"first value\", \"second value\")", 
  "c(\"First value\", \"Second value\")", "<no example>", "<no example>", 
  "<no example>", "<no example>", "<no example>", "\"NL\" || c(\"NL\", \"BE\")", 
  "\"NL\" || c(\"NL\", \"BE\")", "<<validValues>>", "<<validValues>>", 
  "<<validValues>>", "<<validValues>>", "<<validValues>>", "rawTable(10, 5, 15, 10) || rawTable(31, 87, 21, 54, 25, 32, ncol=3)", 
  "\"example1\" || \"another_identifier_example\" || \"finalExample\"", 
  "\"example1\" || c(\"id1\", \"id2\")", "\"example1\" || c(\"id1\", \"id2\")", 
  "\"0000-0003-2814-9483\"", NA), validation = c("all(is.na(VALUE)) || (is.numeric(VALUE) && (length(VALUE) == 1))", 
  "all(is.na(VALUE)) || (is.numeric(VALUE))", "all(is.na(VALUE)) || (is.numeric(VALUE) && (VALUE%%1==0) && (length(VALUE) == 1))", 
  "(is.numeric(VALUE) && (VALUE%%1==0) && (length(VALUE) == 1))", 
  "all(is.na(VALUE)) || (is.numeric(VALUE) && all(VALUE%%1==0))", 
  "all(is.na(VALUE)) || (is.numeric(VALUE) && all(nchar(VALUE)==4))", 
  "all(is.na(VALUE)) || (is.numeric(VALUE) && all(nchar(VALUE)==4)) && (length(VALUE) == 1)", 
  "all(is.na(VALUE)) || (is.character(VALUE) && length(VALUE) == 1)", 
  "is.null(VALUE) || all(is.na(VALUE)) || (is.character(VALUE) && length(VALUE) == 1)", 
  "all(is.na(VALUE)) && !is.null(VALUE) && (nchar(VALUE) > 0)", 
  "all(is.na(VALUE)) || (is.character(VALUE))", "(!any(is.na(VALUE))) && (is.character(VALUE))", 
  "rxs_findEntity(node, \"<<entityRef>>\", VALUE)", "all(is.na(VALUE)) || (!is.na(VALUE) && rxs_findEntity(node, \"<<entityRef>>\", VALUE))", 
  "all(is.na(VALUE)) || (!is.na(VALUE) && rxs_findFieldInEntity(node, \"<<fieldRef>>\", VALUE))", 
  "is.null(VALUE) || all(is.na(VALUE)) || (!is.na(VALUE) && rxs_findEntity(node, \"<<entityRef>>\", VALUE))", 
  "is.null(VALUE) || all(is.na(VALUE)) || (!is.na(VALUE) && rxs_findFieldInEntity(node, \"<<fieldRef>>\", VALUE))", 
  "all(is.na(VALUE)) || (all(VALUE %in% countryCodeList()))", "(all(VALUE %in% countryCodeList()))", 
  "all(is.na(VALUE)) || (VALUE %in% <<validValues>>)", "is.null(VALUE) || all(is.na(VALUE)) || (VALUE %in% <<validValues>>)", 
  "(VALUE %in% <<validValues>>)", "all(is.na(VALUE)) || all(VALUE %in% <<validValues>>)", 
  "all(is.na(VALUE)) || (VALUE %in% <<validValues>>)", "all(is.na(VALUE)) || (class(VALUE) == \"matrix\")", 
  "(length(VALUE)==1) && grepl(\"[a-zA-Z][a-zA-Z0-9_]*\", VALUE)", 
  "all(is.na(VALUE)) || all(grepl(\"[a-zA-Z][a-zA-Z0-9_]*\", VALUE))", 
  "is.null(VALUE) || all(is.na(VALUE)) || all(grepl(\"[a-zA-Z][a-zA-Z0-9_]*\", VALUE))", 
  "all(is.na(VALUE)) || grepl(\"([0-9][0-9][0-9][0-9]-){3}[0-9][0-9][0-9][0-9]\", VALUE)", 
  "!is.na(VALUE)"), error = c("NAME is not numeric, or contains more than one value.", 
  "NAME is not a numeric vector.", "Error about NAME here.", "For entity NAME, you have to extract an integer (i.e., a whole number). You extracted VALUE.", 
  "Error about NAME here.", "When importing extracted value 'NAME', I encountered an error: it is not one or more numeric values of exactly four digits.", 
  "When importing extracted value 'NAME', I encountered an error: it is not a single numeric value of exactly four digits, instead the value is VALUE", 
  "Error about NAME here.", "Error about NAME here.", "This string (character value) must be entered.", 
  "Error about NAME here.", "Entity NAME cannot be empty (\"NA\"), and you have to extract one or more text strings. You extracted VALUE.", 
  "Enter a valid reference to another entity - or remove unused entities.", 
  "Error about NAME here.", "Error about NAME here.", "Error about NAME here.", 
  "Error about NAME here.", "Error about NAME here.", "For entity NAME, you have to extract an ISO 3166-1 alpha-2 country code (always two letters). You extracted VALUE.", 
  "Error about NAME here.", "Error about NAME here.", "Entity NAME cannot be empty (\"NA\"); you have to extract something, specifically, one of the specified values. You extracted VALUE.", 
  "Error about NAME here.", "Error about NAME here.", "Error about NAME here.", 
  "The identifier provided as NAME contains invalid characters (i.e. something that is not a-z, A-Z, 0-9, or _) or does not start with a letter.", 
  "The identifier provided as NAME contains invalid characters (i.e. something that is not a-z, A-Z, 0-9, or _) or does not start with a letter.", 
  "The identifier provided as NAME contains invalid characters (i.e. something that is not a-z, A-Z, 0-9, or _) or does not start with a letter.", 
  "An ORCID always contains exactly four sets of four digits, separated by a dash. However, the value that was extracted for NAME does not match that pattern.", 
  NA)), row.names = c(NA, -30L), class = "data.frame")



rxsTemplateSpec$definitions <-

  structure(list(term = "Example Term", definition = "Example definition of the term."), row.names = c(NA, 
  -1L), class = "data.frame")



rxsTemplateSpec$instructionSheet <-

  structure(list(heading = c("Welcome!", "Naming the Rxs file", 
  "General extraction instructions", "Validating your extraction results", 
  "Extract construct definitions", "Extracting a measurement or manipulation instrument", 
  "Extracting multiple studies", "How to create an identifier", 
  "Future reference", "Minimal specification of a measurement  or manipulation instrument", 
  "Full specification of a questionnaire", "How to create an identifier"
  ), description = c("Welcome to the extraction instructions for EMPATHS-1, the first systematic review in the EMPATHS project. If this is new to you, you may want to start at [https://archeologists.opens.science/empaths.html](https://archeologists.opens.science/empaths.html). This PDF with extraction instructions is available from [https://archeologists.opens.science/empaths-1/extractor-instructions](https://archeologists.opens.science/empaths-1/extractor-instructions).In this project, the focus is on construct definitions and measurement methods. Therefore, during extraction, these are the main entities that you will spend time on. In addition to the brief extraction instructions specified in these instructions and in the extraction script (.Rxs file), where you will register the extracted data, more extensive instructions are provided here.\n\nPlease start by reading these instructions carefully, as well as the extraction instructions at [https://archeologists.opens.science/empaths-1/extraction](https://archeologists.opens.science/empaths-1/extraction). The instructions have two parts: this first part contains general instructions. The second part, starting from \"Entity overview (list)\", contains entity-specific extraction instructions that will also be included in the Rxs Template (the \"R Extraction Script Template\"). That template is where you will conduct the extraction.\n\nYou will start any extraction by copying that template file to a new filename, and then opening that new file to enter the extracted information.", 
  "The filename should follow this format:\n\n\"name_year_sourceId_extractorId.rxs.rmd\"\n\nWhere 'name' is the first word in the last (family) name of the first author, stripped of all characters other than a-z or A-Z; 'year' is the year of publication of the source; 'sourceId' is the source's unique identifier (the ShortDOI if available; otherwise, the QURID); and 'extractorId' is the extractor's unique identifier (i.e., *your* identifier).", 
  "To extract information from the sources, you scroll through the Rxs Template (that you just stored under a new name) and specify what you found for each entity.\n\nYou usually enter information by replacing the NULL with the entity content.\n\nUsually, if something is not reported, replace the NULL with NA (also without quotes).\n\nIf you extract a number, you can usually just replace the NULL with that number. If you extract text, make sure to use double quotes around the text string.\n\nSometimes, you can extract multiple values (you can see this in the entity extraction instructions or in the instructions for the value template). In that case, you place them within a \"concatenator\" or \"combiner\": c(). For example, c(1, 2, 3) for numbers, or c(\"one\", \"two\", \"three\") for text strings.", 
  "If you completed an R Extraction Script (.Rxs.Rmd file), you can (and should) immediately verify whether everything went well. There are two ways to do this. First, there's the Extraction Validation App (EVA). Eva lives is at [https://opens.science/apps/eva](https://opens.science/apps/eva), and can validate your completed extraction script regardless of where you performed the extraction. Second, if you performed the extraction in RStudio, you can render the extraction script with CTRL-ALT-K. This will also produce the validation report, showing which entities validated and what is imported if your extraction script is parsed.", 
  "You extract the empathy definition in a so-called \"clustering entity\" or \"list entity\": a set of closely related entities placed closely together in the Rxs template. If you don't use RStudio for extraction (and so do not benefit of syntax coloring), this can look a bit confusing; clustering entities contain the entity itself (often with default value NULL), immediately followed on the same line by a comment (starting with three hashes, \"###\") with the entity's description, extraction instruction, and the corresponding value template description. Because this is a lot of text, editors (such as Notepad++ and RStudio) will often apply soft word wrapping (splitting long lines and displaying them over multiple lines to prevent them from disappearing off the right side of the screen). You may want to study this clustering entity closely the first time.\n\nThe first entity in this clustering entity is the Empathy Construct Identifier for this specific definition. This is used to allows multiple empathy constructs to be defined in (and extracted from) the same source. However, you have to specify an Empathy Construct Identifier even if a source only has one definition: in other words, you *always* have to specify it. Remember: identifiers can only contains letters, digits, and underscores, and must start with a letter.\n\nThe second entity in this clustering entity is the empathy definition. When looking for a definition of empathy, start by using your source viewer (e.g. if the source is in PDF format, it may be your browser (e.g. Firefox) or a dedicated PDF viewer such as Sumatra or Adobe Acrobat) and use the search/find functionality to look for the text string \"empathy\" (assuming the source was written in English). Ignore definitions in the abstract. If the first occurrence of the construct name is accompanied by its definition, as the authors use it in their work, copy that definition into the extraction script. An example of a (very very brief) definition you might encounter is \"Empathy is the ability to understand and relate to the emotions and experiences of others and to effectively communicate that understanding.\" (note that definitions can also be much longer).\n\nHowever, if the first occurrence is accompanied by a definition that the authors discuss, but not as a definition they use themselves but rather e.g. to introduce readers to the definitions that exist, move to the next occurrence. Similarly, if the first occurrence of the word is not accompanied by a definition at all, move to the next occurrence. For each occurrence, repeat this evaluation: are the authors defining what exactly empathy is? In other words, which parts of the human psyche they consider constituting empathy, and which they consider to reflect other constructs?\n\nOnce you extracted the first fragment (i.e. one or more sentences), repeat your search to see whether the authors provide additional aspects of their definition further on in the introduction. If they do, extract those as well. Extract fragments that occur at different places on the text as separate text elements (e.g. `c(\"first bit\", \"second bit\")`.\n\nIf the authors do not provide an explicit definition, then they may instead cite another source (e.g. an article or a book) and refer to the definition there as the one they use. In that case, obtain the shortdoi for that source, and extract that, in the full URL form (e.g. \"[https://doi.org/gf6btx](https://doi.org/gf6btx)\"). This will enable us to later automatically identify all such URLs, and so categorize sources as either providing their own definition, providing no definition, or citing a definition from elsewhere in the literature (as well as compile a list of such references). If they cite a source that does not have a DOI, consult with the EMPATHS-1 coordinators, Jennifer Gutsell and/or Gjalt-Jorn Peters.\n\nIf the authors do not define empathy but also do not cite another source as providing the definition they use, extract NA to signify that the definition is missing from the source. Similarly, if authors are not explicit about their definition, extract NA. If authors only provide a definition of empathy in the abstract, report that in the comments field in this clustering entity.\n\nIf a source is written in a language that you do not understand, extract \"lang\" as construct definition. This will allow us to later try to find somebody who *can* read that language.\n\nFinally, some sources may contain multiple empathy constructs. In that case, extract them into separate entities. To do this, copy the block starting with the line containing \"START: empathyConstruct (REPEATING)\" and ending with the line containing \"END: empathyConstruct (REPEATING)\". Then complete both entities for the second empathy constructs, and repeat until you extracted all different empathy constructs in the source. (An example of such a paper is ns9s; see [https://doi.org/ns9s](https://doi.org/ns9s) for the PDF and [URL] for the completed Rxs file.)", 
  "When extracting a measurement or manipulation (entities `empathyMeasureId` and `empathyManipulationId`), you specify their unique identifier. This identifier is taken from [https://archeologists.opens.science/empathy-measures](https://archeologists.opens.science/empathy-measures) (from the column marked \"identifier\"). If the instrument you're extracting is already in the list, you can just specify the relevant identifier in the extraction script.\n\nHowever, if it does not yet exist, you have to add it. To do this, visit [https://opens.science/apps/elsa](https://opens.science/apps/elsa), create an identifier, and add into the first column. Then specify the rest of the information as described in section \"Specifying measurement instruments and/or manipulations\" in [https://archeologists.opens.science/extraction](https://archeologists.opens.science/extraction).\n\nJust like definitions, a study can contain multiple measurement instruments or manipulation instruments. Again, copy the relevant block, from the line with \"START: empathyMeasure (REPEATING)\" to the line with \"END: empathyMeasure (REPEATING)\" for multiple measures, and from the line starting with \"START: empathyManipulation (REPEATING)\" to the line ending with \"END: empathyManipulation (REPEATING)\" for multiple manipulations.\n\nConversely, a study may not contain any measurement instruments or manipulations. In that case, you can specify \"noMeasure\" as value of `empathyMeasureId` and leave NULL as `empathyMeasureConstructId`, or \"noManipulation\" as `empathyManipulationId` and leave NULL as `empathyManipulationConstructId`.", 
  "Sometimes, a source reports on multiple studies. If the studies use different measurement instruments or manipulation instruments, copy the study block like you may have copied definition blocks, measurement instrument blocks, or manipulation instrument blocks before. However, study blocks are larger, and themselves contain 'repeating' container entities (specifically, the measurement instrument blocks and the manipulation instrument blocks are specified within the respective study).\n\nTo copy the study block, copy the lines in between the line with \"START: singleStudyContainer (REPEATING)\" to the line with \"END: singleStudyContainer (REPEATING)\". As you'll see, this is quite a large part of the Rxs file. Also note that you may have to specify the population for each study separately.", 
  "To create a unique identifier for a TOM, TOQ, or TOI, you can either use the R package {psyverse} or the Elsa app. To use Elsa, visit [https://opens.science/apps/elsa](https://opens.science/apps/elsa). Identifiers follow the following format. They start with a brief lowercase sequence of letters that is often an acronym or abbreviation of the instrument's name (e.g. 'iri', 'bespt', and 'epitome'). This is followed by a number: the number of items in the measurement instrument; 0 for a manipulation; or 00 for continuous measurement such as EEG. That is followed by the language of the measurement instrument in ISO 639-3 code (see the extraction instructions for extracting the language a source was written in). That is followed by an underscore, and then the last identifier bit as produced by Elsa.", 
  "In the future, we will specify the extracted measurement instruments and manipulations in an open repository. For that stage a number of instructions are included here. **During the extraction phase of this project, you can ignore this; this is simply retained here for future reference.**\n\nIf it is a questionnaire, you can choose to specificy it as a TOQ (\"Tabulated Open Questionnaire\") specification, enabling importing it into the questionnaire repository at https://operationalizations.com. This is not yet possible for measurement instruments that do not consist of questions and for manipulations; those have to be specified as TOM (\"Tabulated Open Metadata\") specifications. Depending on what you choose, follow the corresponding set of instructions below.", 
  "To specify a TOM (\"Tabulated Open Metadata\") specification, you need to complete these steps:\n\n1) visit [https://archeologists.opens.science/empathy-tabulated-specs](https://archeologists.opens.science/empathy-tabulated-specs)\n2) open \"TOM-spec---bespt0eng_7rtpjgf3\"\n3) save a copy under a different name but in the same folder.\n4) create an identifier prefix (see the procedure below for details) and enter it in cell B3\n4) visit https://opens.science/apps/elsa, enter the prefix, and create an identifier\n5) enter the result in cell B4 as UMID\n6) complete the other fields\n7) open the spreadsheet at [https://archeologists.opens.science/empathy-measures](https://archeologists.opens.science/empathy-measures) again and add a row with the UMID you just created", 
  "To specify a TOQ (\"Tabulated Open Questionnaire\") specification, you need to complete these steps:\n\n1) visit [https://archeologists.opens.science/empathy-tabulated-specs](https://archeologists.opens.science/empathy-tabulated-specs)\n2) open \"TOQ-spec---eq60eng_7rs8g3bd\"\n3) save a copy under a different name but in the same folder.\n4) create an identifier prefix (see the procedure below for details) and enter it in cell B3\n4) visit https://opens.science/apps/elsa, enter the prefix, and create an identifier\n5) enter the result in cell B4 as UQID\n6) complete the other fields\n7) open the spreadsheet at [https://archeologists.opens.science/empathy-measures](https://archeologists.opens.science/empathy-measures) again and add a row with the UQID you just created", 
  "To create a unique identifier for a TOM, TOQ, or TOI, you can either use the R package {psyverse} or the Elsa app. To use Elsa, visit https://opens.science/apps/elsa. Identifiers follow the following format. They start with a brief lowercase sequence of letters that is often an acronym or abbreviation of the instrument's name (e.g. 'iri', 'bespt', and 'epitome'). This is followed by a number: the number of items in the measurement instrument; 0 for a manipulation; or 00 for continuous measurement such as EEG. That is followed by the language of the measurement instrument in ISO 639-3 code (see the extraction instructions for extracting the language a source was written in). That is followed by an underscore, and then the last identifier bit as produced by Elsa."
  )), row.names = c(NA, -12L), class = "data.frame")



rxsTemplateSpec$textsSheet <-

  structure(list(textIdentifier = c("openingRemarks", "closingRemarks", 
  "extractionOverview_list_intro", "extractionOverview_compact_intro"
  ), content = c("Welcome to the R Extraction Script (.rxs.Rmd file) for this source!\nThis version of the Rxs template was produced at <<date>>.\n\nYou can now start extracting. If you haven't yet studied the extractor instructions, please do so first. They are located at\nhttps://archeologists.opens.science/empaths-1/extractor-instructions\n\nIf you're all set, good luck! 💪", 
  "Well done! You are now done extracting this source. Great job!!!\r\n\r\nNow, please knit the R Extraction Script into an HTML file and carefully check whether you entered everything correctly, since it will cost much less time to correct any errors, now that you still have this source in your mind, than later on when you'll have to dive into it all over.", 
  "This is an overview of the entities to extract, their titles and descriptions, and other details that will become part of the extraction script template that will be used for the actual extraction.", 
  "This is an overview of the entities to extract, their titles and descriptions, and other details that will become part of the extraction script template that will be used for the actual extraction."
  )), row.names = c(NA, -4L), class = "data.frame")



rxsTemplateSpec$textsList <-

  list(openingRemarks = c(content = "Welcome to the R Extraction Script (.rxs.Rmd file) for this source!\nThis version of the Rxs template was produced at <<date>>.\n\nYou can now start extracting. If you haven't yet studied the extractor instructions, please do so first. They are located at\nhttps://archeologists.opens.science/empaths-1/extractor-instructions\n\nIf you're all set, good luck! 💪"), 
      closingRemarks = c(content = "Well done! You are now done extracting this source. Great job!!!\r\n\r\nNow, please knit the R Extraction Script into an HTML file and carefully check whether you entered everything correctly, since it will cost much less time to correct any errors, now that you still have this source in your mind, than later on when you'll have to dive into it all over."), 
      extractionOverview_list_intro = c(content = "This is an overview of the entities to extract, their titles and descriptions, and other details that will become part of the extraction script template that will be used for the actual extraction."), 
      extractionOverview_compact_intro = c(content = "This is an overview of the entities to extract, their titles and descriptions, and other details that will become part of the extraction script template that will be used for the actual extraction."))



rxsTemplateSpec$errorOnFailingValidation <-

  FALSE



rxsTemplateSpec$rxsRootName <-

  "source"



rxsTemplateSpec$yamlMetadata <-

  NULL

```

Planning: Screening

(link to corresponding SysRevving chapter)

Example: …

Planning: Search

(link to corresponding SysRevving chapter)

Example: We will search using the Ebsco interface in the PsycINFO and Ebsco E-journals databases, and we will use PubMed (using its own interface).

We will only search in titles, and our conceptual query consists of two main terms (substance synonyms and determinant synonyms), where the first main term is split per set of synonyms for each substance.

In the Ebsco query syntax, the query is:

(TI (((ecstasy OR mdma) OR (coke OR cocaine) OR (GHB) OR (LSD) OR (ketamine OR "special K")) AND (determinants OR factors OR reasons)))

That will be used for the PsycINFO and Ebsco E-Journals databases.

In the PubMed query synax, the query is:

(((ecstasy[Title] OR mdma[Title]) OR (coke[Title] OR cocaine[Title]) OR (GHB[Title]) OR (LSD[Title]) OR (ketamine[Title] OR "special K"[Title])) AND (determinants[Title] OR factors[Title] OR reasons[Title]))

Preregistration

(link to corresponding SysRevving chapter)

### Note: this chunk doesn't need to be evaluated (i.e. chunk option "eval" is
### set to FALSE), but in case it is, it writes the template to a different
### file than the version with content added and included in the next chunk.
### (For a list of included packages, see data(package='preregr'))

preregr::form_to_rmd_template(
    "genSysRev_v1",
    file = file.path(scriptPath, "preregistration-autogenerated.Rmd"),
    includeYAML = FALSE
);

### Note also that the preregistration form contains a level 2 heading

Inclusive Systematic Review Registration Form

Section: Metadata

Target discipline

target_discipline

Psychology

Title

title

Empathy and its components – conceptualization, operationalization, and measurement

Author(s) / contributor(s)

authors

(in alphabetical order based on family names) Jillian Franks; Jennifer N. Gutsell; Gjalt-Jorn Peters; Oscar Sun

Tasks and roles

tasks_and_roles

Will be copy-pasted from what {comma} produces based on the google sheet at https://docs.google.com/spreadsheets/d/16If0nGsL3xzfJRZA7UWh9wQL6xVIsq4Z-WfajMVQmT8/edit?usp=sharing.

Section: Review methods

Type of review

type_of_review

Scoping Review

Review stages

review_stages

Preparation, Search, Extraction, Synthesis (note: we will not screen articles; see the screening section)

Current review stage

current_stage

Preparation

Start date

start_date

2025-01-01

End date

end_date

2025-03-01

Background

background

Empathy plays a pivotal role in people’s socio-emotional well-being. In light of its significance, research on empathy has experienced considerable growth in the last two decades. Yet, the existing literature lacks clear construct definitions and agreed-upon measures that capture the multifaceted nature of empathy. There is a growing consensus suggesting that empathy can be viewed as a broad, overarching term encompassing at least three distinct sub-constructs that represent critical dimensions of empathy: an affective component involving emotions, a cognitive component related to understanding, and the act of sharing experiences. Additionally, a certain degree of self-other differentiation and a motivational component – the desire to promote others’ well-being or alleviate their suffering is often integral to the empathic experience.

Despite this conceptual framework, the extent to which empirical studies align with this view of empathy and its constituent elements remains unclear. We are planning to conduct a large-scale scoping review to evaluate how empirical research approaches the measurement and manipulation of empathy and its components. Our review aims to address questions regarding which components of empathy receive significant attention and which remain underexplored, as well as how these components are operationalized and measured. Furthermore, this scoping review will culminate in the creation of a publicly accessible database containing machine-readable data, which can serve as a valuable resource for future systematic reviews and meta-analyses.

Subsequent research could utilize our new database to explore questions like how different components of empathy affect various outcome measures differentially. Similarly, investigating the factors that either facilitate or impede these empathy components and their impact on empathy itself could be a promising future direction stemming from this project.

Primary research question(s)

primary_research_question

How does empirical research in psychology approach the conceptualization, measurement and manipulation of empathy and its components?

Secondary research question(s)

secondary_research_question

To be decided by entire intitial group of co-authors

Expectations / hypotheses

expectations_hypotheses

We expect a large amount of heterogeneity and relatively little convergence between conceptualizations, measurement, and manipulations. We also expect these patterns to differ by component of empathy, with some being better defined, operationalized, and measured while others might be understudied.

Dependent variable(s) / outcome(s) / main variables

dvs_outcomes_main_vars

Main variables: conceptualizations, manipulations, and measures of empathy

Independent variable(s) / intervention(s) / treatment(s)

ivs_intervention_treatment

To be decided by entire intitial group of co-authors

Additional variable(s) / covariate(s)

additional_variables

TBD - by entire intitial group of co-authors

Software

software

Funding

funding

None for now

Conflicts of interest

cois

We declare that we have no conflicts of interest.

Overlapping authorships

overlapping_authorships

Anyone who is co-author on a publication will not be involved in the extraction phase of that publication. We will compare a list of all co-author publications to the included sources to avoid presenting a co-author with self-authored papers to extract.

Section: Search strategy

Databases

databases

PsycINFO

Interfaces

interfaces

We will use EBSCO to search PsycINFO.

Grey literature

grey_literature

We will not search the grey liteature.

Inclusion and exclusion criteria

inclusions_exclusion_criteria

For now we do not have any exclusion criteria but we will discuss the posibility of excluding non-human animals with the full group of collaborators.

Query strings

query_strings

TI empathy

Search validation procedure

search_validation_procedure

We won’t employ any validation procedure because we are aiming to include the entire literature that has empathy in the title.

Other search strategies

other_search_strategies

None used

Procedures to contact authors

procedure_for_contacting_authors

In the first phase of the project, we will not contact authors, but we might decide to do so for future phases of the project. This will then be described in a new pre-registration

Results of contacting authors

results_of_contacting_authors

Not Applicable

Search expiration and repetition

search_expiration_and_repetition

We will strive to develop an infrastructure for keeping the database updated. We do not yet have repetition timepoints specified

Search strategy justification

search_strategy_justification

We aim to capture the the heterogenity of definitions and conceptualizations of empathy in the psychological academic literature. PsycINFO covers most psychological literature and we don’t expect any bias in terms of journals that are included in PsycINFO but would be included in other search engines. We are searching only titles because we are only intersted in articles that primarily focus on empathy. We don’t expect that articles that do not use the term empathy but instead use a derivative term systematically use different definitions of empathy.

Miscellaneous search strategy details

misc_search_strategy_details

There are no other relevant details

Section: Screening

Screening stages

screening_stages

There will be no screening and therefore no screening stages to specify here

Screened fields / masking

screened_fields_masking

There will be no screening and therefore no masking

Used exclusion criteria

used_exclusion_criteria

There will be no screening and therefore no exclusion criteria - UPDATE IF NECESSARY

Screener instructions

screener_instructions

There will be no screening and therefore no screener instructions to specify here

Screening reliability

screening_reliability

Unspecified

Screening reconciliation procedure

screening_reconciliation_procedure

Unspecified

Sampling and sample size

sampling_and_sample_size

We’ll include all search results in the review

Screening procedure justification

screening_procedure_justification

We will not do screening because we are interested in all articles that study empathy as one of their main constructs and it is unlikely that if the authors mention empathy in the title they do not study empathy as one of the main constructs. Therefore we expect screening to yield very few exclusions, hence not warranting the additional effort and resources necessary for screening

Data management and sharing

screening_data_management_and_sharing

We will publicly share files in BibTeX, RIS, CSV, and XLSX

Miscellaneous screening details

misc_screening_details

There are no other details to specify

Section: Extraction

Entities to extract

entities_to_extract

The entities we will extract are described in detail in the Rxs specification, as well as in the extraction script template and the extractor instructions. These are available in the OSF project at https://osf.io/5j82t (for the most current version) or in the files frozen along with this preregistration.

Extraction stages

extraction_stages

We will have two stages: a training/calibration stage to improve the extractor instructions (i.e. the entity descriptions, instructions, and potentially value/data types (e.g. improving categories etc)), where the first 6 sources are extracted by three extractors each, so that first two sources extracted by extractor A are also extracted by extractors B and C; the second two sources of extractor A are also extracted by extractors B and D (for example), and the last two sources of extractor A are also extracted by extractors E and F (e.g.). This maximized ‘matches’ between extractors. Based on the results, the extractor instructions (etc) will be updated, after which the training stage is repeated for a new batch of sources. Once the extractors feel that they align sufficiently, the remaining sources will be extracted independently in batches of 50 sources.

Extractor instructions

extractor_instructions

The extractor instructions are available in the OSF project at https://osf.io/5j82t (for the most current version) or in the files frozen along with this preregistration.

Extractor blinding

extractor_blinding

Extractors will not be masked.

Extraction reliability

extraction_reliability

We do not plan to have independent extraction, and so, will not compute agreement.

Extraction reconciliation procedure

extraction_reconciliation_procedure

We do not plan to have independent extraction, and so, have nothing to reconcile (except from during the training/calibration stage.

Extraction procedure justification

extraction_procedure_justification

Although we acknowledge that independent extraction would yield high-quality results, given the large-scale scope of this undertaking, that does not seem feasible in this first phase. However, once the initial database has been established, we may involve other collaborators and/or students and/or citizens to realize double extraction.

Data management and sharing

extraction_data_management_and_sharing

Everything will be shared through the OSF repo at https://osf.io/5j82t in .rxs.rmd files as well as a variety of files with rectangular data (most likely in .RData, .omv, .sav, .xlsx, and .csv files).

Miscellaneous extraction details

misc_extraction_details

There are no additional details.

Section: Synthesis and Quality Assessment

Planned data transformations

planned_data_transformations

We do not plan any data transformations.

Missing data

missing_data

We will simply register missing data, but since we plan no analyses that might be biased by missing data, we will take no further steps.

Data validation

data_validation

We will not engage in data validation, but we will describe the quality of the reporting.

Quality assessment

quality_assessment

This review is itself about quality assessment, so we consider observations about quality our results.

Synthesis plan

synthesis_plan

We will code the definitions and measurement instruments as qualitative data using and export to the Reproducible Open Coding Kit, and then import the results back. We will then conduct descriptive analyses.

Criteria for conclusions / inference criteria

criteria_for_conclusions

We test no hypotheses and as such have no formal inferential criteria. In addition, our aims are descriptive, and we have no criteria for drawing conclusions about those descriptives.

Synthesist masking

synthesis_masking

We have not yet established a procedure for the synthesis, but given the exploratory nature of the project, in combination with the research question not being related to analyst degrees of freedom, it is unlikely that we will have multiple synthesists.

Synthesis reliability

synthesis_reliability

We will not have multiple synthesists.

Synthesis reconciliation procedure

synthesis_reconciliation_procedure

We will not have multiple synthesists.

Publication bias analyses

publication_bias

We cannot look at publication bias, since we do not look at effect sizes or other results.

Sensitivity analyses / robustness checks

sensitivity_analysis

We do not plan any sensitivity analyses or robustness checks (and would not know how to design those given the kind of data we will extract).

Synthesis procedure justification

synthesis_procedure_justification

We feel that the nature of the analyses does not require a very comprehensive analysis plan; our results will be descriptive.

Synthesis data management and sharing

synthesis_data_management_and_sharing

Everything will be shared through the OSF repo at https://osf.io/5j82t, most likely in R files and .omv files.

Miscellaneous synthesis details

misc_synthesis_details

We have no additional details.

preregr::prereg_spec_to_pdf(
  preregrObject,
  file = file.path(preregPath, "registration-1---preregistration.pdf"),
  author = rmarkdown::metadata$author
);

Example: …

Execution

Execution: Search

(link to corresponding SysRevving chapter)

Example: The queries are entered into the specified interfaces to search the specified databases, separately for each database. The three RIS-files are stored using the following filename convention: YYYY-MM-DD_interface_database_originalFileName.ris.

Importing the search hits

# searchResults <-
#   metabefor::import_search_results(
#     searchPath,
#     dirRegex="2023-02-03"
#   );

This is the number of hits we have for each database:

# knitr::kable(
#   table(searchResults$bibHitDf$originDatabase),
#   col.names = c("Database", "Number of records")
# );

We also see that only a minority of the records has a DOI - at least one that was correctly recognized by synthesisr:

# knitr::kable(
#   table(!is.na(searchResults$bibHitDf$doi)),
#   col.names = c("DOI present?", "Number of records")
# );

Deduplication

### Temporary deactivation to run script

# infoAboutDuplicates <-
#   metabefor::check_duplicate_sources(
#     searchResults$bibHitDf
#   );
# 
# moreInfoAboutDuplicates <-
#   attr(infoAboutDuplicates, "duplicateInfo");
# 
# duplicateChecking <-
#   table(
#     moreInfoAboutDuplicates$fullMatch_year & 
#       moreInfoAboutDuplicates$fullMatch_title &
#       moreInfoAboutDuplicates$fullMatch_author
#   );
# 
# searchResults$bibHitDf$duplicate <-
#   ifelse(
#     infoAboutDuplicates,
#     "duplicate",
#     ""
#   );
# 
# table(searchResults$bibHitDf$duplicate);

Execution: Screening

(link to corresponding SysRevving chapter)

Example: …

Screening stage 1

###-----------------------------------------------------------------------------
### Process first search batch
### Note that these are sorted by batch
###-----------------------------------------------------------------------------

# ### Generate and add quasi-unique record identifiers; note that the origin
# ### *must* be hardcoded to preserve the same QURIDs for every record. The first
# ### record should get "qurid_7mtttgrb".
# searchResults$bibHitDf$qurid <-
#   metabefor::generate_qurids(
#     nrow(searchResults$bibHitDf),
#     origin = as.POSIXct("2023-02-06 15:39:43 CET")
#     );
# 
# screenerPackages <-
#   metabefor::write_screenerPackage(
#     bibliographyDf = searchResults,
#     outputPath = screeningPath,
#     screeners = c("fm2", "il1", "av5"),
#     screenerFieldsPrefix = "stage1_",
#     basename = "stage1_",
#     duplicateField = "duplicate"
#   );

### Potentially, to screen with revtools:
# revtools::screen_titles(bibHitDf[[1]]);

# ###-----------------------------------------------------------------------------
# ### Import files
# ###-----------------------------------------------------------------------------
# 
# filesToImport <-
#   list.files(
#     screeningPath,
#     recursive = TRUE,
#     pattern = "2023-02-28.*bib",
#     full.names = TRUE
#   );
# 
# screenerAcronyms <-
#   gsub("^.*stage1_([a-zA-Z0-9]+)\\.bib$",
#        "\\1",
#        filesToImport);
# 
# # screening_stage1_imported_1 <-
# #   lapply(
# #     filesToImport,
# #     bibtex::read.bib
# #   );
# 
# screening_stage1_imported_2 <-
#   lapply(
#     filesToImport,
#     RefManageR::ReadBib
#   );
# names(screening_stage1_imported_2) <- screenerAcronyms;
# 
# screening_stage1_imported_2_df <-
#   lapply(
#     screening_stage1_imported_2,
#     as.data.frame
#   )
# names(screening_stage1_imported_2_df) <- screenerAcronyms;
# 
# ### Fix wrong column
# # screening_stage1_imported_2_df$av5$screener_av5_stage_1 <-
# #   screening_stage1_imported_2_df$av5$screener_av5_stage_2;
# 
# getScreenerCols <-
#   lapply(
#     screenerAcronyms,
#     function(x) {
#       return(
#         screening_stage1_imported_2_df[[x]][, c("qurid",
#                                               paste0("screener_", x, "_stage_1"))]);
#     }
#   );
# names(getScreenerCols) <- screenerAcronyms;
# 
# newDf <-
#   merge(
#     screening_stage1_imported_2_df$fm2,
#     getScreenerCols$il1,
#     by = "qurid"
#   );
# newDf <-
#   merge(
#     newDf,
#     getScreenerCols$av5,
#     by = "qurid"
#   );
# 
# write.csv(newDf,
#           file = file.path(screeningPath, "2023-02-28---stage1_merged.csv"));
# 
# writexl::write_xlsx(
#   newDf,
#   file.path(screeningPath, "2023-02-28---stage1_merged.xlsx")
# );

# newDf <-
#   as.data.frame(
#     readxl::read_xlsx(
#       file.path(screeningPath, "2023-02-28---stage1_merged.xlsx")
#     )
#   );

### Potentially, to screen with revtools:
# revtools::screen_titles(bibHitDf[[1]]);

Execution: Extraction

(link to corresponding SysRevving chapter)

Example: …

# test <-
#   metabefor::rxs_parseExtractionScripts(
#     path = rxsSpecPath,
#     exclude = NULL
#   );

Execution: Synthesis

(link to corresponding SysRevving chapter)

Example: …