How to set-up a long-distance mentoring program: a framework and case description of mentorship in HIV clinical trials
Bibliographic record
Abstract
Mentoring plays an important role in learning and career development. Mentored researchers are more productive and more likely to publish their work. However, mentorship programs are not universally used in most settings or disciplines. Furthermore, successful and mutually beneficial mentoring relationships are not always easy to arrange. Long-distance mentoring relationships are even more difficult to handle and may break down for a wide variety of reasons. Drawing from our experiences with the first Canadian Institutes of Health Research - Canadian HIV Trials Network international postdoctoral fellowship program, we describe the roles of the context, the key mentor and the mentee attributes; goals and expectations; environments, local support, a communication plan, funding, face-to-face contact, multidisciplinary collaboration, co-mentoring, and evaluation as they apply to the successful implementation of a long-distance mentoring program.
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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 | 0.063 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.016 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".