Exploring mentorship in surgery: An interview study on how people stick together
Bibliographic record
Abstract
OBJECTIVE: The objective is to explore the processes contributing to how and why mentors and mentees initiate, maintain and grow in their mentorship relationships in surgery. BACKGROUND: To explore the processes contributing to how and why mentors and mentees initiate, maintain and grow their mentorship relationships in surgery. Evidence suggests that mentorship has a positive impact on physicians' success. Consequently, mentorship programmes have been incorporated into many medicine environments, albeit with variable success. METHODS: We designed an interview-based study using a constructivist grounded theory approach to explore the dynamics of mentorship between junior and experienced surgeons. Recruited mentees were asked to nominate a senior surgeon they identified as a mentor. Both mentee and mentors were then interviewed separately. Transcripts were analysed using constant comparison to a create a final coding framework and to generate themes. RESULTS: We interviewed nine faculty mentors and 10 junior faculty mentees. Our analysis identified key themes describing how to initiate, maintain and grow a mentorship relationship. Mentorship starts with ensuring a 'good fit', persists through satisfying a reciprocal loop with timely communication and deepens the relationship through cycles of mutual investment, learning, and success. Participants also discussed how to navigate through tensions to avoid relationship breakdown, balancing formality and friendship, knowing when to transition a relationship to a new dynamic and finding areas of realistic contribution. CONCLUSIONS: We found that successful mentorship relationships are viewed as dynamic and thus require active investment and shared responsibility between mentees and mentors. Our results also emphasise the value of co-regulation in the relationship, where cycles of mutual investment can contribute to mutual learning and growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".