One of 2 tables from the same source; this one measures Key Features Questions (KFQ). The others are listed under Related tables.

About this table

Description	Tests of medical knowledge (Key Features Questions, KFQ)
Reference	Feray Ekin Çiçek, Müşerref Ülker, Menekşe Özer, Yavuz Selim Kıyak, ChatGPT versus expert feedback on clinical reasoning questions and their effect on learning: a randomized controlled trial, Postgraduate Medical Journal, 2024;, qgae170, https://doi.org/10.1093/postmj/qgae170
DOI	10.1093/postmj/qgae170
Licence	CC BY 4.0
Source data	https://zenodo.org/records/13769970

Classification

age range	Adult (18+)
sample	Educational
construct type	Cognitive/educational
measurement tool	Test
item format	Likert Scale/selected response
primary language(s)	tur
construct name	Key Features Questions (KFQ)

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 2 collections. Each membership records how it was decided:

Longitudinal longitudinal — by rule
RCT rct — by rule

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.

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

Instrument	Key Features Questions on clinical reasoning in urinary tract infection, from a randomised trial of ChatGPT versus expert feedback (Cicek, Ulker, Ozer & Kiyak, 'ChatGPT versus expert feedback on clinical reasoning questions and their effect on learning: a randomized controlled trial', Postgraduate Medical Journal, doi:10.1093/postmj/qgae170); deposit zenodo.org/records/13769970, CC BY 4.0
Mean words per item	23.667
Mean characters per item	147.333
Mean characters per response	2
Flesch-Kincaid grade level	13.437

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, gilbert_95through96.R. 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
`treat`	IRW standard Treatment group assignment in experimental studies.	made in the build script
`wave`	IRW standard Longitudinal wave indicator.	made in the build script
Respondent covariates
`cov_male`	Covariate	made in the build script
`cov_repeat_year`	Covariate	made in the build script

What item codes and covariate values stand for is in the codebook released with the source data.

Respondent uniqueness. No respondent in this table is unique on the single covariate above: every one of them shares their combination with someone else. The IRW republishes the demographics its sources published and never at finer granularity: see the data standard.

Get the data

Download CSVno account needed Browse on Redivisexplore and query Croissant 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("gilbert_meta_95")

# Python
pip install irw

import irw
df = irw.fetch("gilbert_meta_95")

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 gilbert_meta_95, clear

How to cite

@article{_i_ek_2024, title={ChatGPT versus expert feedback on clinical reasoning questions and their effect on learning: a randomized controlled trial}, ISSN={1469-0756}, url={http://dx.doi.org/10.1093/postmj/qgae170}, DOI={10.1093/postmj/qgae170}, journal={Postgraduate Medical Journal}, publisher={Oxford University Press (OUP)}, author={Çiçek, Feray Ekin and Ülker, Müşerref and Özer, Menekşe and Kıyak, Yavuz Selim}, year={2024}, month=dec }

Responses	2,760
Respondents	115
Items	12
Response categories	63
Responses per respondent	24
Responses per item	230
Density	2
Longitudinal	TRUE

IRW version	v674
Redivis dataset	item_response_warehouse v78.0
Redivis dataset DOI	10.57761/yzj1-v654
Manifest pin for this IRW version	v78.0
Metadata source	irw_meta v59.0

gilbert_meta_95