Issues in the Mentor–Mentee Relationship in Academic Medicine: A Qualitative Study
Bibliographic record
Abstract
PURPOSE: To explore the phenomenon of the mentor-mentee relationship and to characterize this relationship among people who have obtained early career support from a government funding agency, in order to facilitate the development of future mentorship programs. METHOD: A qualitative study was completed involving clinician scientists who were awarded early career support from a provincial funding agency (Alberta Heritage Foundation for Medical Research, Edmonton, Alberta, Canada) and their mentors. Individual, semistructured interviews were completed, and transcripts of interviews were analyzed using a grounded theory approach. RESULTS: Interviews with 21 population health or clinician investigators (mentees) and seven mentors were completed from October to December 2006. Several themes were identified including the experience with mentorship, experience of being assigned a mentor versus self-identification, roles of a mentor, characteristics of good mentoring, barriers to mentorship, and possible mentorship strategies. Participants believed mentorship to be important, but several experienced significant difficulty with finding mentors and establishing productive relationships. CONCLUSIONS: Challenges exist within academic medicine around ensuring that clinician scientists receive appropriate mentorship. Strategies to enhance the mentorship process were identified, including the development of formal mentorship initiatives, the creation of workshops organized by funding agencies in partnership with universities, and the development and evaluation of a mentorship training initiative for mentors and mentees. These findings can be applied to any academic health sciences institution.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".