Five hats of effective leaders: teacher, mentor, coach, supervisor and sponsor
Bibliographic record
Abstract
BACKGROUND/AIM: Teaching, mentoring, coaching, supervising and sponsoring are often conflated in the literature. In this reflection, we clarify the distinctions, the benefits and the drawbacks of each approach. We describe a conceptual model for effective leadership conversations where leaders dynamically and deliberately 'wear the hats' of teacher, mentor, coach, supervisor and/or sponsor during a single conversation. METHODS: As three experienced physician leaders and educators, we collaborated to write this reflection on how leaders may deliberately alter their approach during dynamic conversations with colleagues. Each of us brings our own perspective and lens. RESULTS: We articulate how each of the 'five hats' of teacher, mentor, coach, supervisor and sponsor may help or hinder effectiveness. We discuss how a leader may 'switch' hats to engage, support and develop colleagues across an ever-expanding range of contexts and settings. We demonstrate how a leader might 'wear the five hats' during conversations about career advancement and burn-out. CONCLUSION: Effective leaders teach, mentor, coach, supervise and sponsor during conversations with colleagues. These leaders employ a deliberate, dynamic and adaptive approach to better serve the needs of their colleagues at the moment.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".