Characteristics of Successful and Failed Mentoring Relationships
Bibliographic record
Abstract
PURPOSE: To explore the mentor-mentee relationship with a focus on determining the characteristics of effective mentors and mentees and understanding the factors influencing successful and failed mentoring relationships. METHOD: The authors completed a qualitative study through the Departments of Medicine at the University of Toronto Faculty of Medicine and the University of California, San Francisco, School of Medicine between March 2010 and January 2011. They conducted individual, semistructured interviews with faculty members from different career streams and ranks and analyzed transcripts of the interviews, drawing on grounded theory. RESULTS: The authors completed interviews with 54 faculty members and identified a number of themes, including the characteristics of effective mentors and mentees, actions of effective mentors, characteristics of successful and failed mentoring relationships, and tactics for successful mentoring relationships. Successful mentoring relationships were characterized by reciprocity, mutual respect, clear expectations, personal connection, and shared values. Failed mentoring relationships were characterized by poor communication, lack of commitment, personality differences, perceived (or real) competition, conflicts of interest, and the mentor's lack of experience. CONCLUSIONS: Successful mentorship is vital to career success and satisfaction for both mentors and mentees. Yet challenges continue to inhibit faculty members from receiving effective mentorship. Given the importance of mentorship on faculty members' careers, future studies must address the association between a failed mentoring relationship and a faculty member's career success, how to assess different approaches to mediating failed mentoring relationships, and how to evaluate strategies for effective mentorship throughout a faculty member's career.
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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 | 0.007 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".