cordova_2026_who5

1,250 responses from 250 respondents to 5 items.

One of 3 tables from the same source; this one measures Well-being (WHO-5 adaptation). The others are listed under Related tables.

About this table

DescriptionGeneral well-being (WHO-5 adaptation), 5 items, 0 = never to 3 = always. Non-commissioned officers of the Peruvian National Police (Spanish administration). N=250.
ReferenceCordova Ruelas J (2026). Carga de trabajo, autoeficacia profesional y bienestar en suboficiales de la Policia Nacional del Peru [Data set]. Zenodo. https://doi.org/10.5281/zenodo.20418454
LicenceCC BY 4.0
Source datahttps://zenodo.org/records/20418454

Size and shape

Responses1,250
Respondents250
Items5
Response categories4
Responses per respondent5
Responses per item250
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleWorkplace
construct typeAffective/mental health
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)spa
construct nameWell-being (WHO-5 adaptation)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, cordova_2026_police_wellbeing.py. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.

ColumnWhat it isSource column
IRW standard columns
idIRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).partcipante
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Respondent covariates
cov_ageCovariate edad
cov_marital_statusCovariate estado civil
cov_rankCovariate grado policial
cov_sexCovariate sexo
cov_unitCovariate unidad de servicio
cov_years_serviceCovariate años de servicio

What item codes and covariate values stand for is in the source’s codebook, content.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("cordova_2026_who5")
# Python
pip install irw

import irw
df = irw.fetch("cordova_2026_who5")

How to cite

@misc{https://doi.org/10.5281/zenodo.20418454,
  doi = {10.5281/ZENODO.20418454},
  url = {https://zenodo.org/doi/10.5281/zenodo.20418454},
  author = {Cordova Ruelas, Jakeline},
  title = {Carga de trabajo, autoeficacia profesional y bienestar en suboficiales de la Policia Nacional del Perú},
  publisher = {Zenodo},
  year = {2026},
  copyright = {Creative Commons Attribution 4.0 International}
}

Version and provenance

IRW versionv584
Redivis datasetitem_response_warehouse_6 v5.11
Redivis dataset DOI10.57761/qahq-j855
Manifest pin for this IRW versionv5.11
Metadata sourceirw_meta v44.0