mugisha_2026_ehr_competence

3,648 responses from 228 respondents to 16 items.

One of 3 tables from the same source; this one measures Evaluation of users' EHR competence (EvUEHR, 16 items). The others are listed under Related tables.

About this table

Description16 agreement items (1-5) on users' EHR competence; N = 228 hospital staff (doctors, nurses/midwives, ICT/lab technicians, administrators); cov_age_group, cov_gender, cov_marital, cov_position, cov_qualification, cov_years_experience.
ReferenceMugisha, B. (2026). Digital Competence-based Framework [data]. Zenodo. https://doi.org/10.5281/zenodo.19387267
LicenceCC0 1.0
Source datahttps://zenodo.org/records/19387267

Size and shape

Responses3,648
Respondents228
Items16
Response categories5
Responses per respondent16
Responses per item228
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleWorkplace
construct typeOpinion/attitude
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng
construct nameEvaluation of users' EHR competence (EvUEHR, 16 items)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, mugisha_2026_ehr_competence.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).made in the build script
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Respondent covariates
cov_age_groupCovariate Age
cov_genderCovariate Gender
cov_maritalCovariate Marital_Status
cov_positionCovariate Position
cov_qualificationCovariate Qualification
cov_years_experienceCovariate Years_Experience

What item codes and covariate values stand for is in the codebook released with the source data.

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("mugisha_2026_ehr_competence")
# Python
pip install irw

import irw
df = irw.fetch("mugisha_2026_ehr_competence")

How to cite

@misc{https://doi.org/10.5281/zenodo.19387267,
  doi = {10.5281/ZENODO.19387267},
  url = {https://zenodo.org/doi/10.5281/zenodo.19387267},
  author = {Brian, Mugisha},
  title = {Digital Competence-based Framework},
  publisher = {Zenodo},
  year = {2026},
  copyright = {Creative Commons Zero v1.0 Universal}
}

Version and provenance

IRW versionv584
Redivis datasetitem_response_warehouse_7 v1.1
Redivis dataset DOI10.57761/7pd3-sh31
Manifest pin for this IRW versionv1.0
Metadata sourceirw_meta v44.0