Pre-clerkship Exploration of Underrepresented Specialties: Participant Perceptions
Notice bibliographique
Résumé
Background: Background: Exposure to specialties significantly influences medical student career decisions; however, many students feel they are not adequately introduced to particular specialties until the end of their undergraduate training, if at all. Therefore, the Pre-clerkship Residency Exploration Program (PREP) was established. PREP was designed to reduce concerns regarding career decisions, while increasing exposure to specialties that traditionally receive less exposure in medical school curricula. Methods: PREP was a two-week elective available to second year medical students (n = 40) comprising five components: clinical electives, panel discussions, procedural skills circuits, simulations, and specialty-specific workshops. Participants rotated through ten electives and engaged in panel discussions focused on career choices and decisions. Skills circuits and simulations introduced students to procedures and scenarios they could encounter during PREP elective rotations. Specialty-specific workshops were held by several departments to build interest and introduce students to under-represented specialties. Results: PREP was assessed using the Kirkpatrick model, a framework that evaluates the effectiveness of training. PREP significantly increased students’ comfort with making career decisions, while reducing concerns related to a lack of exposure to various specialties (p < 0.0001) and time constraints with determining career options (p < 0.0001). Furthermore, PREP directly impacted career aspirations with 80.6% of participants changing their top-three career choices after completing the program. PREP is a valuable addition to medical school education and offers a novel approach to supporting students’ informed career decisions as well as increase their exposure to specialties which are underrepresented in medical school curricula. Discussion: We are currently in discussion with several Canadian medical schools about implementing PREP at their universities. Future research will analyze if participation in PREP translates to increased application rates to underrepresented specialties. To accomplish this objective, we will follow cohorts of PREP participants through the residency matching process and compare outcomes with historical data.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 tête enseignante, 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 ».