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Record W4410335212 · doi:10.18192/uojm.v15i1.6727

Identifying Gaps in the Teaching of Medical Literature Critical Appraisal Skills: A Needs Assessment Survey of Medical Students

2025· article· en· W4410335212 on OpenAlexaffvenueabout
Sarah Elias, Michael Reaume, Rakesh V. Patel

Bibliographic record

VenueUniversity of Ottawa Journal of Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedical educationCritical appraisalMedicinePsychologyAlternative medicinePathology

Abstract

fetched live from OpenAlex

Objective: Acquisition of critical appraisal skills during medical training is essential for providing high-quality evidence-based patient care. To ensure the effective and durable acquisition and application of these skills, within an overcrowded undergraduate curriculum, the learning needs of medical students must be better understood. The objective of this survey was to explore medical students’ medical literature critical appraisal skills and determine their educational needs. Methods: A web-based survey was administered to University of Ottawa medical students over a 2-month period. The survey captured demographic information, educational experiences, the perceived value of critical appraisal skills, and learning preferences for the development of these skills. Proportions were reported for both categorical and ordinal variables. Results: Fifty-nine students completed the survey. The majority of respondents reported that they were lacking both competence (57%) and confidence (75%) in critical appraisal. The most common content delivery methods for teaching critical appraisal skills were lectures and seminars. However, journal club, case-based learning and journal articles were perceived by respondents as being more effective content delivery methods. Conclusion: Most students recognize the clinical practice value of critical appraisal but report lacking competence and/or confidence for successfully employing these skills for patient care. Interestingly, students’ preferred content delivery methods differed from those most commonly utilized in our undergraduate medical curricula, highlighting a shortcoming in the teaching of evidence-based medicine. ---------- Objectif : L’acquisition de compétences d’évaluation critique au cours de la formation médicale est essentielle pour fournir aux patients des soins de qualité fondés sur des données probantes. Pour garantir une acquisition et une application efficace et durable de ces compétences, dans le cadre d’un programme d’études de premier cycle surchargé, les besoins d’apprentissage des étudiants en médecine doivent être mieux compris. L’objectif de ce sondage était d’explorer les compétences des étudiants en médecine en matière d’évaluation critique de la littérature médicale et de déterminer leurs besoins en matière d’éducation. Méthodes : Un sondage en ligne a été administré aux étudiants en médecine de l’Université d’Ottawa sur une période de deux mois. L’enquête a recueilli des informations démographiques, des expériences éducatives, la valeur perçue des compétences d’évaluation critique et les préférences d’apprentissage pour le développement de ces compétences. Les proportions ont été rapportées pour les variables catégorielles et ordinales. Résultats : Cinquante-neuf étudiants ont répondu à l’enquête. La majorité des répondants ont indiqué qu’ils manquaient à la fois de compétences (57%) et de confiance (75%) en matière d’évaluation critique. Les méthodes les plus courantes pour enseigner les compétences en matière d’évaluation critique sont les cours magistraux et les séminaires. Cependant, les clubs de lecture, l’apprentissage basé sur des cas et les articles de journaux ont été perçus par les répondants comme étant des méthodes de transmission de contenu plus efficaces. Conclusion : La plupart des étudiants reconnaissent la valeur de l’évaluation critique dans la pratique clinique, mais déclarent manquer de compétences et/ou de confiance pour utiliser avec succès ces compétences dans les soins aux patients. Il est intéressant de noter que les méthodes d’enseignement préférées des étudiants diffèrent de celles qui sont le plus souvent utilisées dans nos programmes d’études médicales de premier cycle, ce qui met en évidence une lacune dans l’enseignement de la médecine fondée sur des données probantes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.037
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.412
Teacher spread0.392 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
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".

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Citations0
Published2025
Admission routes3
Has abstractyes

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