Mentoring and role models in recruitment and retention: A study of junior medical faculty perceptions
Bibliographic record
Abstract
PURPOSE: This study explored the views of junior faculty toward informing mentorship program development. METHOD: Mixed sampling methodologies including questionnaires (n = 175), focus groups (female, n = 4; male, n = 4), and individual interviews (female n = 10; male, n = 9) of junior faculty were conducted in clinical departments at one academic health sciences center. RESULTS: Questionnaire results indicated that having role models increased commitment to an academic career; mentorship experience during residency training was a high incentive to pursue an academic career; and junior faculty did have identifiable mentorship experiences. Focus group results revealed that mentoring as well as the presence of role models a few years ahead of the junior faculty would promote career development. Females preferred similar age role models who spoke the same language, particularly in the area of promotion. Females identified several challenges and issues including a lack of researcher role models, a range of perceptions regarding the merits of formal versus informal mentoring, and the idea that mentors should provide advice on promotion and grants. Males valued advice on finances while females wanted advice on work-life balance. CONCLUSIONS: Mentorship emerged as an important factor in academic faculty recruitment and retention, with varying perceptions of how it should be institutionalized. Role models were viewed as important for retention, and a paucity of mid-career, female researcher role models suggests a gap to be filled in future programmatic efforts.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".