Twelve tips for developing effective mentors
Bibliographic record
Abstract
Mentoring is often identified as a crucial step in achieving career success. However, not all medical trainees or educators recognize the value of a mentoring relationship. Since medical educators rarely receive training on the mentoring process, they are often ill equipped to face challenges when taking on major mentoring responsibilities. This article is based on half-day workshops presented at the 11th Ottawa International Conference on Medical Education in Barcelona on 5 July 2004 and the annual meeting of the Association of American Medical Colleges in Boston on 10 November 2004 as well as a review of literature. Thirteen medical faculty participated in the former and 30 in the latter. Most participants held leadership positions at their institutions and mentored trainees as well as supervised mentoring programs. The workshops reviewed skills of mentoring and strategies for designing effective mentoring programs. Participants engaged in brainstorming and interactive discussions to: (a) review different types of mentoring programs; (b) discuss measures of success and failure of mentoring relationships and programs; and (c) examine the influence of gender and cultural differences on mentoring. Participants were also asked to develop an implementation plan for a mentoring program for medical students and faculty. They had to identify student and faculty mentoring needs, and describe methods to recruit mentors as well as institutional reward systems to encourage and support mentoring.
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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 | 0.033 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".