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Enregistrement W4410011001 · doi:10.1111/medu.15714

Caffeinate a resident: Brewing career connections

2025· article· en· W4410011001 sur OpenAlexfundaboutno aff
Sarah Moussa, Nehal Islam, Angana Datta

Notice bibliographique

RevueMedical Education · 2025
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueManagement and Marketing Education
Établissements canadiensnon disponible
Organismes subventionnairesMcGill University
Mots-clésBrewingMedical educationPsychologyMedicineFamily medicineFood scienceChemistry

Résumé

récupéré en direct d'OpenAlex

Mentoring relationships aimed at medical students can enhance educational satisfaction and support career development.1 Most interactions with postgraduate medical education (PGME) colleagues are limited to senior clerkship years, leaving pre-clinical students with minimal mentorship opportunities early on. This reality is often present in other health care fields where clinical practicums are integrated into the later years of training. This early gap in mentorship may limit guidance early on. We launched the Caffeinate a Resident programme in 2018 as part of McGill University's Medical Student Society, pairing medical students with residents in their preferred specialties through informal coffee or tea meetings, helping facilitate mentorship opportunities between colleagues in undergraduate medical education (UGME) and PGME. Students rank their specialty preferences and are matched with volunteering residents. Since its inception, the programme has connected a total of 776 students with 729 residents. Each year, an average of 28 PGME programmes participate (SD: 5.8). The University of British Columbia Medical Undergraduate Society has since implemented a similar programme. Caffeinate a Resident distinguishes itself by providing early informal and relaxed mentorship between trainees following similar footsteps. Since 2018, we have streamlined the program with no-cost automation via free educational institutional licences (Power Automate, Microsoft Forms, Excel; Microsoft Corporation, USA), requiring about 10 hours to automate and operate annually from two dedicated volunteers. Beverages are independently arranged and purchased by the students and residents. As a result, the programme has been designed to operate at zero cost. The program follows a five-step process: stakeholder engagement, participant online registration, match database creation, communication of pairings and post-program online survey evaluation. Since its inception in 2018, the Caffeinate a Resident programme has revealed four key takeaways as they relate to (1) efficiency and sustainability, (2) programme engagement, (3) adaptability and (4) organizational culture. First, we discovered that automation is valuable in educational innovations, minimizing human error and significantly reducing time and effort requirements. A programme that initially involved dozens of hours of commitment over weeks can now be completed in 1 week, ensuring less burden on the programme's student leaders. Second, we learned that to ensure student and resident engagement, the programme must ally itself with existing institutions, such as student and resident associations, programme directors and the UGME and PGME offices. When all stakeholders were involved early, we had a 1.5-fold increase in PGME programmes participating in 2023 (26 to 39 programmes). Third, we learned that such a programme can be adaptable to other training contexts outside our own, namely, at the University of British Columbia. This initiative has the potential to be adapted and successfully integrated into other allied health programmes. Lastly, we learned through informal feedback amongst our peers that implementing such mentorship dyads is an important step towards fostering a greater sense of belonging amongst the UGME and PGME through low-stakes and informal settings, an impact reflected by the sustained engagement and annual participation of both trainees and PGME programmes. Sarah Moussa: Conceptualization; writing—original draft; writing—review and editing; project administration; resources; visualization; supervision. Nehal Islam: Writing—original draft; writing—review and editing; project administration; resources. Aurgho Datta: Writing—original draft; writing—review and editing; project administration; resources. We thank past and present Ambassadors for Comprehensive Education (ACE) sub-committee of the McGill Medical Student Society and all McGill resident doctors who have participated in this programme since 2018. Special thanks to the Association of McGill Residents (ARM), the Postgraduate Medical Education Dean and McGill's programme directors for their invaluable support. None. Not applicable. The data that support the findings of this study are available from the corresponding author upon reasonable request.

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)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,699
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

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

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 Opus0,009
Tête enseignante GPT0,266
Écart entre enseignants0,257 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
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'admission2
Résumé présentoui

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