mugisha_2026_ehr_use

3,648 responses from 228 respondents to 16 items.

One of 3 tables from the same source; this one measures Effective use of the EHR system (EUSEHR, 16 items). The others are listed under Related tables.

About this table

Description16 agreement items (1-5) on effective use of the EHR system; 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 nameEffective use of the EHR system (EUSEHR, 16 items)

Codebook

This codebook is reconstructed by the IRW from the IRW data standard. 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).not traced
itemIRW standard Item identifier.not traced
respIRW standard Response value.not traced
Respondent covariates
cov_age_groupCovariate not traced
cov_genderCovariate not traced
cov_maritalCovariate not traced
cov_positionCovariate not traced
cov_qualificationCovariate not traced
cov_years_experienceCovariate not traced

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_use")
# Python
pip install irw

import irw
df = irw.fetch("mugisha_2026_ehr_use")

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