Mapping the Mentorship Landscape in Otolaryngology–Head and Neck Surgery Training Programs: A Cross‐Canadian Survey
Bibliographic record
Abstract
OBJECTIVE: This study aims to explore the current landscape of mentorship within Canadian Otolaryngology-Head and Neck Surgery (OHNS) programs by investigating the experiences and perspectives of OHNS trainees and program directors (PDs). STUDY DESIGN: A cross-sectional survey study. METHODS: Anonymized online questionnaires were sent to all residents and PDs of the 13 accredited OHNS residency programs across Canada. The questionnaires collected qualitative and quantitative information about the type of mentorship (formal vs informal) programs implemented, as well as individuals' experiences and opinions on mentorship. RESULTS: Of residents, 57.1% (92/161) completed the survey, whereas 84.6% (11/13) of PDs completed the survey. Of residents, 45.7% (42/92) participated in formal mentorship programs and 72.8% (67/92) participated in informal mentorship programs. The PDs perceived the importance of formal mentorship at 3.0/5. Residents reported greater satisfaction with informal mentorship relationships compared to formal mentorship (4.4/5 vs 3.7/5, P < .01) due to a more organic initiation of relationship and a better personality match. The main areas for improvement of current mentorship programs included the availability of mentors, networking opportunities, and protected time for encounters. CONCLUSION: Surgical residents found informal mentorship to be more beneficial than formal mentorship. However, residency programs should provide more guidance and structure to optimize hybrid mentorship opportunities and mentor selection/availability. Mentorship training or development resources for attending physicians and feedback opportunities are essential for efficient relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".