2,760 responses from 115 respondents to 12 items.
One of 2 tables from the same source; this one measures Key Features Questions (KFQ). The others are listed under Related tables.
| 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 |
| Responses | 2,760 |
|---|---|
| Respondents | 115 |
| Items | 12 |
| Response categories | 63 |
| Responses per respondent | 24 |
| Responses per item | 230 |
| Density | 2 |
| Longitudinal | TRUE |
| 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) |
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 | 13.584 |
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.
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("gilbert_meta_95")
# Python
pip install irw
import irw
df = irw.fetch("gilbert_meta_95")
@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 }
| IRW version | v569 |
|---|---|
| Redivis dataset | item_response_warehouse v71.0 |
| Redivis dataset DOI | 10.57761/f5n4-k797 |
| Manifest pin for this IRW version | v70.0 |
| Metadata source | irw_meta v43.0 |