4,443 responses from 202 respondents to 22 items.
One of 4 tables from the same source; this one is the mbi table. The others are listed under Related tables.
| Description | Maslach Burnout Inventory (HSS), 22 items, 0-6 Likert, surgical trainees |
|---|---|
| Reference | Dominguez LC, Stassen L, de Grave W, Sanabria A, Alfonso E, Dolmans D. (2018) Taking control: Is job crafting related to the intention to leave surgical training? PLoS ONE. |
| DOI | 10.1371/journal.pone.0197276 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0197276.s001 |
| Responses | 4,443 |
|---|---|
| Respondents | 202 |
| Items | 22 |
| Response categories | 7 |
| Responses per respondent | 21.995 |
| Responses per item | 201.955 |
| Density | 1.000 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Workplace |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | spa |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, dominguez_2018_job_crafting.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.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
id | IRW 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 |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Respondent covariates | ||
cov_age | Covariate | made in the build script |
cov_gender | Covariate | made in the build script |
cov_institution | Covariate | made in the build script |
cov_institution_type | Covariate | made in the build script |
cov_residency_level | Covariate | made in the build script |
What item codes and covariate values stand for is in the codebook released with the source data.
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("dominguez_2018_mbi")
# Python
pip install irw
import irw
df = irw.fetch("dominguez_2018_mbi")
@article{Dominguez_2018, title={Taking control: Is job crafting related to the intention to leave surgical training?}, volume={13}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0197276}, DOI={10.1371/journal.pone.0197276}, number={6}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Dominguez, Luis Carlos and Stassen, Laurents and de Grave, Willem and Sanabria, Alvaro and Alfonso, Edgar and Dolmans, Diana}, editor={Koniaris, Leonidas G.}, year={2018}, month=June, pages={e0197276} }
| IRW version | v566 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v13.0 |
| Redivis dataset DOI | 10.57761/w8zx-td92 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v43.0 |