Mentoring needs of distributed medical education faculty at a Canadian medical school: a mixed-methods descriptive study.
Notice bibliographique
Résumé
INTRODUCTION: The Schulich School of Medicine & Dentistry in London, Ontario, has a mentorship program for all full-time faculty. The school would like to expand its outreach to physician faculty located in distributed medical education sites. The purpose of this study was to determine what, if any, mentorship distributed physician faculty currently have, to gauge their interest in expanding the mentorship program to distributed physician faculty and to determine their vision of the most appropriate design of a mentorship program that would address their needs. METHODS: We conducted a mixed-methods study. The quantitative phase consisted of surveys sent to all distributed faculty members that elicited information on basic demographic characteristics and mentorship experiences/needs. The qualitative phase consisted of 4 focus groups of distributed faculty administered in 2 large and 2 small centres in both regions of the school's distributed education network: Sarnia, Leamington, Stratford and Hanover. Interviews were 90 minutes long and involved standardized semistructured questions. RESULTS: Of the 678 surveys sent, 210 (31.0%) were returned. Most respondents (136 [64.8%]) were men, and almost half (96 [45.7%]) were family physicians. Most respondents (197 [93.8%]) were not formal mentors to Schulich faculty, and 178 (84.8%) were not currently being formally mentored. Qualitative analysis suggested that many respondents were involved in informal mentoring. In addition, about half of the respondents (96 [45.7%]) wished to be formally mentored in the future, but they may be inhibited owing to time constraints and geographical isolation. Consistently, respondents wished to have mentoring by a colleague in a similar practice, with the most practical being one-on-one mentoring. CONCLUSION: Our analysis suggests that the school's current formal mentoring program may not be applicable and will require modification to address the needs of distributed faculty.
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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 | 0,004 | 0,006 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 découleClassification
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