The new mentee: Exploring Gen Z women medical students' mentorship needs and experiences
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Notice bibliographique
Résumé
PURPOSE: The incoming Canadian cohort of medical students is comprised mainly of individuals from Generation Z (Gen Z; born between 1997 and 2012), with greater than 50% of applicants identifying as female. A gap remains in our understanding of Gen Z women learners in their challenges in navigating medical education, their expectations for their medical careers and the influences that have impacted their worldview. This study explored the needs, values, and experiences of Gen Z women medical students and the impact of these factors on mentorship expectations among this population that will soon be entering the workforce. METHODS: Upon receiving ethics approval from the University of Toronto Research Ethics Board, semi-structured interviews were conducted (February-May 2021) with 15 Gen Z women students from 14 English-speaking Canadian medical schools who had given written consent to participate. An iterative constant comparative team approach was utilised in which the interview guide and sampling were adjusted as the data evolved. Transcripts were line by line coded into categories, then grouped into themes using descriptive analysis. RESULTS: These socially aware learners described how society had afforded them greater opportunities for expression, which gave them a sense of feeling advantaged over older generations. However, participants paradoxically expressed feelings of powerlessness and commented on tensions they experienced when interacting with older generation physician mentors, especially during conversations on social justice issues. They also highlighted instances of biased mentorship specific to their gender. Participants emphasised a desire for inclusive mentorship that considered the mentee's identity and intersectionality. CONCLUSIONS: The growing number of women learners in Canadian medical schools necessitates a re-evaluation of mentorship delivery. Mentors must adapt by integrating Gen Z ideals to overcome mentorship challenges.
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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 | 0,020 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 découle