EMSC_Kuan-chin_2023

693 responses from 99 respondents to 7 items.

About this table

DescriptionCommunication Skills
ReferenceChen, K. C. J., Bartman, I., Pugh, D., Topps, D., Desjardins, I., Forgie, M., & Archibald, D. (2023). Experience of introducing an electronic health records station in an objective structured clinical examination to evaluate medical students’ communication skills in Canada: a descriptive study. Journal of Educational Evaluation for Health Professions, 20, 22-22. Kuan-chin Jean Chen; Ilona Bartman; Debra Pugh; David Topps; Isabelle Desjardins; Melissa Forgie; Douglas Archibald, 2023, "Experience of introducing an electronic health records station in an objective structured clinical examination to evaluate medical students’ communication skills in Canada: a descriptive study", https://doi.org/10.7910/DVN/IFMDCC, Harvard Dataverse, V1
LicenceCC0 1.0
Source datahttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IFMDCC

Size and shape

Responses693
Respondents99
Items7
Response categories5
Responses per respondent7
Responses per item99
Density1
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleEducational
construct typeAffective/mental health, Cognitive/educational
measurement toolObservational rating
item formatLikert Scale/selected response
primary language(s)eng
construct nameEvaluation of Medical Students' Communication Skills in an Objective Structured Clinical Examination (OSCE) Using Electronic Medical Records (EMR)

Item text

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

InstrumentCommunication Skills
Mean words per item1.857
Mean characters per item16.143
Mean characters per response53.905
Flesch-Kincaid grade level17.811

Columns

iditemresp

Get the data

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

Or load it directly in R or Python:

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("EMSC_Kuan-chin_2023")
# Python
pip install irw

import irw
df = irw.fetch("EMSC_Kuan-chin_2023")

Version and provenance

IRW versionv393
Redivis datasetitem_response_warehouse v53.0
Redivis dataset DOI10.57761/4g08-xt41
Manifest pin for this IRW versionv53.0
Metadata sourceirw_meta v23.0