bang_2023_health

518 responses from 37 respondents to 14 items.

One of 5 tables from the same source; this one measures SF-12 Health Survey (adapted 14-item). The others are listed under Related tables.

About this table

DescriptionPhysical/mental health scale (14 items), quasi-experimental wellness program for unmarried mothers
ReferenceBang K-S, Kim S, Choi S, Lee G, Kim M, Shin D-A (2023). Effectiveness of a natural wellness group program using interactive real-time video for unmarried mothers: A quasi-experimental study. PLOS ONE, 18(4), e0284340. https://doi.org/10.1371/journal.pone.0284340
DOI10.1371/journal.pone.0284340
LicenceCC BY 4.0
Source datahttps://doi.org/10.1371/journal.pone.0284340.s004

Size and shape

Responses518
Respondents37
Items14
Response categories4
Responses per respondent14
Responses per item37
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleProgram-based
construct typePhysical health/functioning
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)kor
construct nameSF-12 Health Survey (adapted 14-item)

These are classifications the IRW records, not the study’s own words. Some were annotated by hand and some written by an automated tagger whose output is published only where it clears a measured accuracy bar; which values came from which is not recorded per table. Coverage and accuracy per column, and what is held back, are on Tag quality.

This table is in 1 collection. Each membership records how it was decided:

Membership is the IRW’s judgment and is worth checking before you use a collection as a sample. A hand-curated list is a snapshot: it is not re-checked when tables are added, so a table that would qualify may be missing. See Collections.

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, bang_2023_wellness_program.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.

ColumnWhat it isSource column
IRW standard columns
idIRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).no
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
treatIRW standard Treatment group assignment in experimental studies.made in the build script
Respondent covariates
cov_ageCovariate age

What item codes and covariate values stand for is in the source’s codebook, Data dictionary (sheet in S1_Dataset.xlsx).

Respondent uniqueness. 32% of this table’s respondents are alone in their combination of the single covariate above — no one else in the table shares it. That is a necessary condition for re-identification and not re-identification itself, and on a sample this size it is largely arithmetic. The IRW republishes the demographics its sources published and never at finer granularity: see the data standard.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R (no account needed with irw 1.4.0 or later) or Python (needs a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("irw")
library(irw)
df <- irw_fetch("bang_2023_health")
# Python
pip install irw

import irw
df = irw.fetch("bang_2023_health")

Or in Stata, which reads the same no-account CSV as the button above:

* Stata
net install irw, from("https://raw.githubusercontent.com/itemresponsewarehouse/Stata-pkg/main/")

irw_fetch bang_2023_health, clear

How to cite

@article{Bang_2023, title={Effectiveness of a natural wellness group program using interactive real-time video for unmarried mothers: A quasi-experimental study}, volume={18}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0284340}, DOI={10.1371/journal.pone.0284340}, number={4}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Bang, Kyung-Sook and Kim, Sungjae and Choi, Sinyoung and Lee, Gumhee and Kim, Misook and Shin, Da-Ae}, editor={Cwik, Jan Christopher}, year={2023}, month=Apr, pages={e0284340} }

Version and provenance

IRW versionv674
Redivis datasetitem_response_warehouse_3 v15.0
Redivis dataset DOI10.57761/6rg4-6f73
Manifest pin for this IRW versionv15.0
Metadata sourceirw_meta v59.0