3,696 responses from 168 respondents to 22 items.
| Description | Maslach Burnout Inventory (22 items) administered to frontline doctors in Bangladesh during COVID-19 |
|---|---|
| Reference | Rashid F, Erfan Uddin RAM, Mehedi HMH, Dhar S, Bhuiyan NH, Sattar MA, Chowdhury S (2022). Burnout syndrome among frontline doctors of secondary and tertiary care hospitals of Bangladesh during COVID-19 pandemic. PLOS ONE, 17(11), e0277875. https://doi.org/10.1371/journal.pone.0277875 |
| DOI | 10.1371/journal.pone.0277875 |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.1371/journal.pone.0277875.s003 |
| Responses | 3,696 |
|---|---|
| Respondents | 168 |
| Items | 22 |
| Response categories | 5 |
| Responses per respondent | 22 |
| Responses per item | 168 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Workplace |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | eng |
cov_academic_year_groupcov_age_groupcov_degreecov_duty_hour_groupcov_duty_placecov_duty_typecov_employment_typecov_patient_turnover_groupcov_sexiditemresp
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("rashid_2022_mbi")
# Python
pip install irw
import irw
df = irw.fetch("rashid_2022_mbi")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v7.0 |
| Redivis dataset DOI | 10.57761/pqqn-pm43 |
| Manifest pin for this IRW version | v7.0 |
| Metadata source | irw_meta v23.0 |