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Record W4210477937 · doi:10.2196/preprints.8188

Comparison of the Impact of Wikipedia, UpToDate, and a Digital Textbook on Short-Term Knowledge Acquisition Among Medical Students: Randomized Controlled Trial of Three Web-Based Resources (Preprint)

2017· preprint· en· W4210477937 on OpenAlexaffabout
Michael A. Scaffidi, Rishad Khan, Christopher Wang, Daniela Keren, Cindy Tsui, Ankit Garg, Simarjeet Brar, Kamesha Valoo, Michael Bonert, Jacob F de Wolff, James Heilman, Samir C. Grover

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsUniversity of British ColumbiaMcMaster UniversityUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedical educationResource (disambiguation)Randomized controlled trialMultiple choiceComputer sciencePsychologyMedicineFamily medicineWorld Wide WebSignificant difference

Abstract

fetched live from OpenAlex

<sec> <title>BACKGROUND</title> Web-based resources are commonly used by medical students to supplement curricular material. Three commonly used resources are UpToDate (Wolters Kluwer Inc), digital textbooks, and Wikipedia; there are concerns, however, regarding Wikipedia’s reliability and accuracy. </sec> <sec> <title>OBJECTIVE</title> The aim of this study was to evaluate the impact of Wikipedia use on medical students’ short-term knowledge acquisition compared with UpToDate and a digital textbook. </sec> <sec> <title>METHODS</title> This was a prospective, nonblinded, three-arm randomized trial. The study was conducted from April 2014 to December 2016. Preclerkship medical students were recruited from four Canadian medical schools. Convenience sampling was used to recruit participants through word of mouth, social media, and email. Participants must have been enrolled in their first or second year of medical school at a Canadian medical school. After recruitment, participants were randomized to one of the three Web-based resources: Wikipedia, UpToDate, or a digital textbook. During testing, participants first completed a multiple-choice questionnaire (MCQ) of 25 questions emulating a Canadian medical licensing examination. During the MCQ, participants took notes on topics to research. Then, participants researched topics and took written notes using their assigned resource. They completed the same MCQ again while referencing their notes. Participants also rated the importance and availability of five factors pertinent to Web-based resources. The primary outcome measure was knowledge acquisition as measured by posttest scores. The secondary outcome measures were participants’ perceptions of importance and availability of each resource factor. </sec> <sec> <title>RESULTS</title> A total of 116 medical students were recruited. Analysis of variance of the MCQ scores demonstrated a significant interaction between time and group effects (P&lt;.001, ηg2=0.03), with the Wikipedia group scoring higher on the MCQ posttest compared with the textbook group (P&lt;.001, d=0.86). Access to hyperlinks, search functions, and open-source editing were rated significantly higher by the Wikipedia group compared with the textbook group (P&lt;.001). Additionally, the Wikipedia group rated open access editing significantly higher than the UpToDate group; expert editing and references were rated significantly higher by the UpToDate group compared with the Wikipedia group (P&lt;.001). </sec> <sec> <title>CONCLUSIONS</title> Medical students who used Wikipedia had superior short-term knowledge acquisition compared with those who used a digital textbook. Additionally, the Wikipedia group trended toward better posttest performance compared with the UpToDate group, though this difference was not significant. There were no significant differences between the UpToDate group and the digital textbook group. This study challenges the view that Wikipedia should be discouraged among medical students, instead suggesting a potential role in medical education. </sec>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.412
Teacher spread0.384 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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