Twelve tips for developing effective mentors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 it