Mentorship in postgraduate training programmes: views of Canadian programme directors
Bibliographic record
Abstract
OBJECTIVES: Many academic training programmes have developed mentorship programmes for postgraduate doctors in training, but little is known about the factors that influence their establishment. METHODS: Canadian postgraduate training directors were surveyed to determine views on mentorship and factors associated with the establishment of these programmes. RESULTS: A total of 199 of 344 (58%) programme directors completed an online survey. Overall, 65% of respondents reported that their training programmes had a mentorship programme and 40% felt there was a need for more structured mentorship in training programmes. Univariate analysis showed that mentorship programmes were present significantly more often in larger programmes, internal medicine-based training programmes, and in programmes where the acting programme director had either been part of a mentorship programme during his or her own training or felt that mentorship had played an important role in his or her professional development. In adjusting for covariates using a logistic regression analysis, only those factors directly attributable to a programme director's personal mentoring experiences remained significantly associated with having a mentorship programme. Those who felt that mentorship had played a role in their own careers (P = 0.008, odds ratio [OR] = 3.3, 95% confidence interval [CI] 1.7-6.6) or who had been part of a mentorship programme during their own training (P = 0.01, OR = 6.6, 95% CI 1.4-30.1) were more likely to have an active mentorship programme at their institution. CONCLUSIONS: A need for more structured mentorship was identified for many training programmes. Overall, programme directors' previous mentoring experiences were independently associated with having a mentorship programme.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".