Mentorship perceptions and experiences among academic family medicine faculty: Findings from a quantitative, comprehensive work-life and leadership survey.
Bibliographic record
Abstract
OBJECTIVE: To collect information about the types, frequency, importance, and quality of mentorship received among academic family medicine faculty, and to identify variables associated with receiving high-quality mentorship. DESIGN: Web-based survey of all faculty members of an academic department of family medicine. SETTING: The Department of Family and Community Medicine of the University of Toronto in Ontario. PARTICIPANTS: All 1029 faculty members were invited to complete the survey. MAIN OUTCOME MEASURES: Receiving mentorship rated as very good or excellent in 1 or more of 6 content areas relevant to respondents' professional lives, and information about demographic and practice characteristics, faculty ratings of their local departments and main practice settings, teaching activities, professional development, leadership, job satisfaction, and health. Bivariate and multivariate analyses identified variables associated with receiving high-quality mentorship. RESULTS: The response rate was 66.8%. Almost all (95.0%) respondents had received mentorship in several areas, with informal mentorship being the most prevalent mode. Approximately 60% of respondents rated at least 1 area of mentoring as very good or excellent. Multivariate logistic regression identified 5 factors associated with an increased likelihood of rating mentorship quality as very good or excellent: positive perceptions of their local department (odds ratio [OR] = 4.02, 95% CI 2.47 to 6.54, P < .001); positive ratings of practice infrastructure (OR = 1.86, 95% CI 1.23 to 2.80, P = .003); increased frequency of receiving mentorship (OR = 2.78, 95% CI 1.59 to 4.89, P < .001); fewer years in practice (OR = 1.93, 95% CI 1.19 to 3.12, P = .007); and practising in a family practice teaching unit (OR = 1.51, 95% CI 1.01 to 2.27, P = .040). CONCLUSION: With increasing emphasis on distributed education and community-based teachers, family medicine faculties will need to develop strategies to support effective mentorship across a range of settings and career stages.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".