3,636 responses from 202 respondents to 18 items.
One of 2 tables from the same source; this one is the hweat table. The others are listed under Related tables.
| Description | 18-item Japanese Healthy Work Environment Assessment Tool (HWE-AT-J, 1-5 Likert), main validation sample of critical-care nurses |
|---|---|
| Reference | Kitayama M, Unoki T, Matsuda Y, Matsuishi Y, Kawai Y, Iida Y (2022) Development and initial validation of the Japanese healthy work environment assessment tool for critical care settings. PLoS ONE. |
| DOI | 10.1371/journal.pone.0268124 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0268124.s004 |
| Responses | 3,636 |
|---|---|
| Respondents | 202 |
| Items | 18 |
| Response categories | 5 |
| Responses per respondent | 18 |
| Responses per item | 202 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Workplace |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | jpn |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Japanese version of the Healthy Work Environment Assessment Tool (HWE-AT-J) |
|---|---|
| Mean words per item | 26.167 |
| Mean characters per item | 179.389 |
| Mean characters per response | 2 |
| Flesch-Kincaid grade level | 15.135 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, kitayama_2022_hweat.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 |
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("kitayama_2022_hweat")
# Python
pip install irw
import irw
df = irw.fetch("kitayama_2022_hweat")
@article{Kitayama_2022, title={Development and initial validation of the Japanese healthy work environment assessment tool for critical care settings}, volume={17}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0268124}, DOI={10.1371/journal.pone.0268124}, number={5}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Kitayama, Mio and Unoki, Takeshi and Matsuda, Yui and Matsuishi, Yujiro and Kawai, Yusuke and Iida, Yasuo and Teramoto, Mio and Tatsuno, Junko and Hamamoto, Miya}, editor={Özden, Fatih}, year={2022}, month=May, pages={e0268124} }
| IRW version | v569 |
|---|---|
| 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 |