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Enregistrement W7131832954 · doi:10.1093/acamed/wvaf034

Supporting PhD students entering medical school outside traditional MD pathways

2025· article· en· W7131832954 sur OpenAlexaff
Chloé Lau

Notice bibliographique

RevueAcademic Medicine · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueHealth and Medical Research Impacts
Établissements canadiensUniversity of Toronto
Organismes subventionnairesAmerican Foundation for Suicide Prevention
Mots-clésMedical schoolMEDLINECurriculumClinical clerkshipProfessional development

Résumé

récupéré en direct d'OpenAlex

To the Editor, Medical students who enter MD programs after completing a PhD, but outside of formal MD/PhD streams, face unique yet under-recognized challenges. These individuals often begin their medical training as fully qualified researchers, having completed doctoral work before entry into the MD curriculum. However, unlike their MD/PhD counterparts, they lack structured mentorship, protected research time, and programmatic support to help integrate their prior expertise into clinical training.1,2 This absence of institutional scaffolding can lead to a disorienting transition from expert to novice, feelings of social disconnection from younger peers, and limited opportunities to apply their research background within the rigid timelines of undergraduate medical education. Despite these obstacles, PhD-trained MD students are well-positioned to contribute to the physician-scientist workforce, a cohort that has been steadily declining in recent years and is critical for advancing evidence-based clinical innovation.3,4 At the University of Toronto, the lack of support for this group was particularly salient amid national concerns about the sustainability of clinician-scientist pathways and the underutilization of highly trained individuals within Canada’s healthcare and academic systems.3,4 To address this gap, we expanded the PhDs in MD Group at the University of Toronto, a student-led initiative for MD students who hold PhDs but are not enrolled in formal MD/PhD programs. The group includes students at various stages of medical training, from pre-clerkship to clerkship and electives, who learned of the group through peer networks, word of mouth, or social media outreach. Currently, there is no formal mechanism for identifying or onboarding such students, which contributes to isolation and missed opportunities for institutional engagement. The PhDs in MD Group aims to foster community, provide tailored peer mentorship, and advocate for structural support. We organized support meetings, academic events, and collaborative discussions with faculty to address the misalignment between our research training and the MD curriculum. A key outcome was the establishment of a Postdoctoral Award, modeled after summer research studentships, to provide protected time for scholarly work during medical training. This funding opportunity offered a concrete mechanism for re-engaging with research while reinforcing our dual identity as scientists and clinicians-in-training. Our experience highlights the importance of visibility, peer ­connection, and institutional recognition. The sustainability and impact of such efforts will depend on more formalized recognition and integration. We recommend that medical schools collaborate with academic affairs offices and physician-scientist training program coordinators to proactively identify PhD-trained MD students and offer them structured opportunities for engagement. This may include formalizing group membership, embedding the initiative within institutional reporting structures, and offering access to protected research time and mentorship. As the physician-scientist workforce continues to contract,3,4 supporting PhD-trained medical students represents a scalable and underutilized strategy for advancing academic medicine. Tailored support, rather than a one-size-fits-all approach, is essential to ensure these nontraditional learners are empowered to contribute fully to clinical, academic, and systems-level ­innovation. None declared. The author would like to thank the American Foundation for Suicide Prevention for supporting the first author’s research. Reported as not applicable. None declared. None declared. Reported as not applicable.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,019
score de la tête « metaresearch » (Gemma)0,056
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Incitatifs · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,981
Score d'incertitude au seuil0,452

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0190,056
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0140,003
Communication savante0,0200,007
Science ouverte0,0040,032
Intégrité de la recherche0,0060,010
Charge utile insuffisante (le modèle a refusé de juger)0,1350,025

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.

Tête enseignante Opus0,308
Tête enseignante GPT0,514
Écart entre enseignants0,207 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeQualitatif
DomaineIncitatifs
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentnon

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