Digital Learning Tools: Findings from a National Survey of Canadian Medical Learners
Bibliographic record
Abstract
Objectives: The need for enhanced adoption of digital learning tools into medical education was highlighted by the COVID-19 pandemic. To inform development and implementation of digital tools during training, we designed a survey exploring the current scope of digital learning tool use by medical students and family medicine residents in Canada. Methods: We conducted a national survey of medical students and family medicine residents at 14/17 medical schools across Canada. We used frequency tables and descriptive statistics to summarize the multiple-choice responses and performed a content analysis of the free-text responses to identify recurrent themes. Results: Survey responses indicated that learners value information quality, user experience, and accessibility. Barriers to accessing digital learning tools include cost and usability. Conclusions: Medical educators looking to improve the delivery of medical education should focus on learner experience, removing the aforementioned barriers, and iterative evaluation by learners to maintain relevance, usefulness, and effectiveness. ---------- Objectifs : La nécessité d’adopter davantage les outils d’apprentissage numériques dans l’enseignement médical a été mis en évidence par la pandémie de COVID-19. Afin d’informer le développement et la mise en œuvre de tels outils pendant la formation, nous avons conçu une enquête pour explorer l’étendue actuelle de l’utilisation des outils d’apprentissage numériques par les étudiants en médecine et les résidents en médecine familiale au Canada. Méthodes : Nous avons mené une enquête nationale auprès d’étudiants en médecine et de résidents en médecine familiale dans 14 des 17 facultés de médecine en Canada. Nous avons utilisé des tableaux de fréquence et des statistiques descriptives pour résumer les réponses à choix multiples et avons effectué une analyse du contenu des réponses libres afin d’identifier les thèmes récurrents. Résultats : Les réponses de l’enquête ont indiqué que les apprenants apprécient la qualité de l’information, l’expérience utilisateur et l’accessibilité. Les obstacles à l’accès aux outils d’apprentissage numériques comprenaient le coût et la facilité d’utilisation. Conclusions : Les éducateurs en médecine qui cherchent à améliorer la livraison de l’enseignement médical devraient se concentrer sur l’expérience des apprenants, supprimer les obstacles susmentionnés et utiliser une évaluation itérative par les apprenants pour maintenir la pertinence, l’utilité et l’efficacité.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".