Peer-led learning: a novel approach to promote rural healthcare interest among medical students
Notice bibliographique
Résumé
Introduction: A persistent maldistribution of medical workforce exists across Canada, with rural areas facing a greater physician shortage. Medical education can be instrumental to increase physicians in rural communities, and medical schools have adapted strategies to generate interest in rural careers among medical students. Many of these efforts occur within formal structured curriculum. This study appraises the effectiveness of peer-led learning (PLL) as a novel approach in rural medical education to provide students with a better understanding of rural life and rural medical practice. Methods: This is mixed methods study using a survey and follow-up focus group discussion to evaluate a day-long educational experience organized and led by a medical student to their rural community. Quantitative data were summarized with descriptive statistics. Reflexive thematic analysis was conducted on qualitative insights to describe the students' experiences and perceptions about the educational rural day. Results: Of 54 participants, 50 completed the survey and 13 consented for the follow-up focus group. Most (78%) were female, have non-rural origins (78%), with only 2 having Indigenous status. Majority (61%) have low familiarity with rural medicine. Trustworthiness scores for information about rural life and medical practice were higher for rural-origin peers and rural-origin faculty compared to other sources of information such as government websites, social media, and traditional media. Thematic analysis yielded three main themes: (i) informal teaching facilitated learning, (ii) trust in their peer enabled students to receive information more favorably, and (iii) students gained a better understanding of rural life and medical practice. Conclusion: This study demonstrated that medical students engage differently with peer-led learning activities about rural medical curriculum versus a formal teaching environment. Medical students are cautious about promotional information regarding rural medical education from formal sources but are less skeptical when learning from peers. Information about the way of life and healthcare needs in rural communities may be perceived as more credible and valid if coming from a peer, and hence, is more likely to be received favorably. Thus, when promoting rural education and careers, medical schools should work with rural-origin students, whose messaging may be considered more trustworthy than traditional sources.
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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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».