Engaging Undergraduate Medical Students With Introductory Research Training via an Educational Escape Room: Mixed Methods Evaluation
Notice bibliographique
Résumé
BACKGROUND: Early exposure to research methodology is essential in medical education, yet many students show limited motivation to engage with non-clinical content. Gamified strategies such as educational escape rooms (EERs) may help improve engagement, but few studies have explored their feasibility at scale or evaluated their impact beyond student satisfaction. OBJECTIVE: To assess the feasibility, engagement, and perceived educational value of a large-scale escape room specifically designed to introduce third-year medical students to the principles of diagnostic test evaluation. METHODS: We developed a low-cost immersive escape room based on a fictional diagnostic accuracy study, with six puzzles mapped to five predefined learning objectives: (1) identifying key components of a diagnostic study protocol, (2) selecting an appropriate gold-standard test, (3) defining a relevant study population, (4) building and interpreting a contingency table, and (5) critically appraising diagnostic metrics in context. The intervention was deployed to an entire class of third-year medical students across 12 sessions between March and April 2023. Each session included 60 minutes of gameplay and a 45-minute debriefing. Students completed pre-/post-intervention questionnaires assessing their knowledge of diagnostic test evaluation and perceptions of research training. Descriptive statistics and paired t-tests were used to evaluate score changes; univariate linear regressions assessed associations with demographics. Free-text comments were analyzed using Reinert's hierarchical classification. RESULTS: Among 530 participants, 490 completed the full evaluation. Many participants had limited prior exposure to escape rooms (206/490, 42% had never participated), and most reported low initial confidence with critical appraisal of scientific articles. All student teams completed the scenario, with a mean completion time of 53 (±4) minutes. Mean overall knowledge scores increased from 62/100 (±1) before to 82/100 (±2) after the activity (+32%, p<0.001). Gains were observed across all learning objectives and were not influenced by age, sex, or prior experience. Students rated the EER as highly entertaining (9.1±1.1/10) and educational (8.2±1.5/10). Following the intervention, 87% (393/452) felt more comfortable with critical appraisal of diagnostic test studies, and 79% (357/452) considered the escape room format highly appropriate for an introductory session. Thematic analysis of open-ended feedback identified six clusters, including engagement, teamwork, and perceived usefulness of the pedagogical approach. Word clouds showed a marked shift from negative to positive attitudes toward research training. CONCLUSIONS: This study demonstrates the feasibility and enthusiastic reception of a large-scale, reusable escape room aimed at teaching the fundamental principles of diagnostic test evaluation to undergraduate medical students. While not designed to cover the broader spectrum of research designs or methods, the intervention successfully addressed targeted objectives within a specific area of research appraisal. This approach may serve as a valuable entry point to engage students with evidence-based reasoning and pave the way for deeper exploration of medical research methodology.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,053 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».