4,509 responses from 501 respondents to 9 items.
One of 4 tables from the same source; this one measures Patient Health Questionnaire-9 (PHQ-9). The others are listed under Related tables.
| Description | PHQ-9, stored 1-4 (the file's scoring subtracts 1); 501 elite Chinese athletes; cov_age, cov_gender (code). |
|---|---|
| Reference | Gao, Y., & Wang, L. (2024). A study on the relationship and path between mental health and burnout of Chinese athletes. Frontiers in Psychology, 15, 1422207. |
| DOI | 10.3389/fpsyg.2024.1422207 |
| Licence | CC BY 4.0 |
| Source data | https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_A_study_on_the_relationship_and_path_between_mental_health_and_burnout_of_Chinese_athletes_CSV/26336833 |
| Responses | 4,509 |
|---|---|
| Respondents | 501 |
| Items | 9 |
| Response categories | 4 |
| Responses per respondent | 9 |
| Responses per item | 501 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Adult (18+) |
|---|---|
| sample | Targeted/specific |
| construct type | Affective/mental health |
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
| construct name | Patient Health Questionnaire-9 (PHQ-9) |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, gao_2024_athlete_burnout.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). | index |
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 | Q1 |
cov_gender | Covariate | Q2 |
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("gao_2024_phq9")
# Python
pip install irw
import irw
df = irw.fetch("gao_2024_phq9")
@article{Gao_2024, title={A study on the relationship and path between mental health and burnout of Chinese athletes}, volume={15}, ISSN={1664-1078}, url={http://dx.doi.org/10.3389/fpsyg.2024.1422207}, DOI={10.3389/fpsyg.2024.1422207}, journal={Frontiers in Psychology}, publisher={Frontiers Media SA}, author={Gao, Yun and Wang, Lei}, year={2024}, month=July }
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_5 v8.5 |
| Redivis dataset DOI | 10.57761/41xy-5k56 |
| Manifest pin for this IRW version | v8.5 |
| Metadata source | irw_meta v44.0 |