teo_2021_burnout

7,238 responses from 329 respondents to 22 items.

About this table

DescriptionMaslach Burnout Inventory-Human Services Survey for Medical Personnel, 22 items scored 0 (never) to 6 (every day), allied health professionals at a Singapore tertiary hospital, surveyed 2019. 7,238 responses from 329 respondents on 22 items.
ReferenceTeo, Y. H.; Xu, J. T. K.; Ho, C.; Leong, J. M.; Tan, B. K. J.; Tan, E. K. H.; Goh, W.; Neo, E.; Chua, J. Y. J.; Ng, S. J. Y.; Cheong, J. J. Y.; Hwang, J. Y.; Lim, S. M.; Soo, T.; Sng, J. G. K.; Yi, S. (2021). Factors associated with self-reported burnout level in allied healthcare professionals in a tertiary hospital in Singapore. PLOS ONE, 16(1), e0244338. https://doi.org/10.1371/journal.pone.0244338
DOI10.1371/journal.pone.0244338
LicenceCC BY 4.0
Source datahttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244338

Size and shape

Responses7,238
Respondents329
Items22
Response categories7
Responses per respondent22
Responses per item329
Density1
LongitudinalFALSE

Columns

cov_age_bandcov_caregivercov_earningscov_employmentcov_ethnicitycov_gendercov_hospitalcov_mental_helpcov_mental_illnesscov_night_shiftscov_occupationcov_physical_activitycov_positioncov_residencycov_years_experienceiditemresp

Get the data

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

Or load it directly in R or Python:

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("teo_2021_burnout")
# Python
pip install irw

import irw
df = irw.fetch("teo_2021_burnout")

Version and provenance

IRW versionv393
Redivis datasetitem_response_warehouse_5 v4.0
Redivis dataset DOI10.57761/yvjw-0981
Manifest pin for this IRW versionv4.0
Metadata sourceirw_meta v23.0