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Enregistrement W4414841363 · doi:10.22605/rrh9474

Influences on medical studentsâ clinical school preferences: outcomes from a Rural Clinical School immersion program in Australia

2025· article· en· W4414841363 sur OpenAlexaboutno aff
Sue Garner, R Mark Beattie, Condon, Füller

Notice bibliographique

RevueRural and Remote Health · 2025
Typearticle
Langueen
DomaineHealth Professions
ThématiqueGlobal Health Workforce Issues
Établissements canadiensnon disponible
Organismes subventionnairesAustralian Government
Mots-clésMedical schoolPerceptionQuarter (Canadian coin)Data collectionSchool systemMEDLINE

Résumé

récupéré en direct d'OpenAlex

CONTEXT: In Australia, rural clinical schools (RCSs) were developed to address the maldistribution of the rural medical workforce. Evidence demonstrates that medical students who attend an RCS, or have a rural background, are more likely to become rural doctors. To enhance the likelihood of our graduates from Deakin University becoming rural doctors, we strategically combined these two independent factors and created a dedicated rural training stream (RTS), which commenced in 2022. To support the introduction of the RTS and provide students with an authentic RCS experience, we developed a 3-day RCS immersion program for year 1 students. The broad aim was to provide students with experience and knowledge that would allow them to make an informed clinical school preferencing decision. Despite delivering the same curriculum, each of Deakin University's three RCS campuses are shaped by their distinct clinical setting, community and approach to program delivery. To showcase these individual aspects, each RCS designed a bespoke 3-day immersion program centred around three themes: connecting students to the local Indigenous Country, the community and the clinical school. ISSUE: Historically, our students' clinical school preferences have fluctuated annually, with the majority of students generally electing to remain at the years 1 and 2 urban training location. This phenomenon was unsurprising as the majority of students, with metropolitan backgrounds, had little understanding of what living and learning in a rural community would be like. Clinical school promotional activities, before the introduction of the RTS, were held at the preclinical urban campus. Only a small number of students would visit one or more of Deakin University's five clinical schools on an ad-hoc basis. The available research on how medical students make their clinical school preferencing decisions highlights that both personal and learning needs are considerations. However, we lacked evidence on factors influencing our own students' clinical school decisions. Information provided to prospective students focused solely on the clinical schools, with an absence of practical information about the rural community or Country. The introduction of the RTS and immersion program provided an opportunity to explore medical students' decisions when preferencing clinical schools, offering learnings to enhance the associated policies and procedures. LESSONS LEARNED: The program achieved its overarching aim of providing students with realistic exposure to the RCS environment, with 86.9% agreeing the experience helped them to make informed decisions about their clinical school preferences. The program, initially a pilot, has become embedded in the year 1 curriculum. Participation in the immersion program reduced student hesitancy towards attending an RCS, with over a quarter of initially hesitant students ultimately ranking an RCS as their first preference. Furthermore, there was a significant positive shift in students indicating that they were confident that the RCS would be the best environment for them (p=0.001). The linking of immersion evaluation data and clinical school preference information provided insights into students' perceptions of the program, the RCSs and the factors influencing their preferences. When viewed collectively, it was evident that a review of our clinical school allocation process was warranted. This will be monitored as our RTS develops, particularly with the introduction of preclinical learning campuses (2024) in two prominent rural locations.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,175
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,004
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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.

Tête enseignante Opus0,132
Tête enseignante GPT0,572
Écart entre enseignants0,440 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
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 présentoui

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