4,553 responses from 702 respondents to 10 items.
One of 5 tables from the same source; this one is the social_support table. The others are listed under Related tables.
| Description | Duke Social Support and Stress Scale supportiveness items (10 items, 1-3) from 702 medical students at the Chinese University of Hong Kong, 2017; the "there is no such person" category is coded missing. |
|---|---|
| Reference | Lee KP, Yeung N, Wong C, Yip B, Luk LHF, Wong S (2020). Prevalence of medical students' burnout and its associated demographics and lifestyle factors in Hong Kong. PLOS ONE 15(7): e0235154. https://doi.org/10.1371/journal.pone.0235154 |
| DOI | 10.1371/journal.pone.0235154 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235154 |
| Responses | 4,553 |
|---|---|
| Respondents | 702 |
| Items | 10 |
| Response categories | 3 |
| Responses per respondent | 6.486 |
| Responses per item | 455.300 |
| Density | 0.649 |
| Longitudinal | FALSE |
| age range | Mixed |
|---|---|
| child age (for child-focused studies) | Adolescent (12-18y) |
| sample | Educational |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, lee_2020_medical_students.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 | Age |
cov_clinical_stage | Covariate | clinical_year |
cov_first_degree | Covariate | MBChB_first_degree |
cov_gender | Covariate | Gender |
cov_marital_status | Covariate | Marital_status |
cov_place_of_living | Covariate | Place_of_living |
cov_scholarship | Covariate | Scholarship |
cov_smoke | Covariate | Smoke |
cov_study_year | Covariate 1 = Year 1 · 2 = Year 2 · 3 = Year 3 · 4 = Year 4 · 5 = Year 5 · 6 = Year 6 | Study_year |
Covariate value labels are the source’s own, as the IRW recorded them. What item codes and any unlabelled 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("lee_2020_social_support")
# Python
pip install irw
import irw
df = irw.fetch("lee_2020_social_support")
@article{Lee_2020, title={Prevalence of medical students’ burnout and its associated demographics and lifestyle factors in Hong Kong}, volume={15}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0235154}, DOI={10.1371/journal.pone.0235154}, number={7}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Lee, K. P. and Yeung, Nelson and Wong, Carmen and Yip, Ben and Luk, Lawrence H. F. and Wong, Samuel}, editor={Useche, Sergio A.}, year={2020}, month=July, pages={e0235154} }
| 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 |