Qualification and teaching of peer-student tutors in undergraduate medical education: Does it affect the understanding and acquiring of CanMEDS competencies?
Bibliographic record
Abstract
Objective: The Canadian Medical Education Directives for Specialists (CanMEDS), described by scientists of the Royal College of Physicians and Surgeons of Canada, specify the competencies that medical students should acquire during their studies of human medicine. These include the skills of Medical Expert, Communicator, Collaborator, Manager, Professional, Health Advocate and Scholar. We investigated the opinion of former student tutors that are currently active as clinical residents to understand whether they acquired and applied the CanMEDS competencies more during their student tutoring activities or during their undergraduate medical training. Methods: An online questionnaire with closed and open questions were distributed to residents with work experience as student tutors. A Likert scale was used to assess the relevance of each CanMEDS competency to their current employment and to evaluate the acquisition and application of each CanMEDS competency during undergraduate medical training in comparison to their peer-student tutor employment. We used the sign test for identify significant differences in the acquisition and application of CanMEDS competencies. Results: Of one hundred and twenty-three tutors, twenty-four met the inclusion criteria and fourteen questionnaires were returned. Concerning acquiring the CanMEDS roles Collaborator, Communicator, Professional and Manager there was a significant difference in favour of peer-student tutor employment. Nearly all participants indicated that they noticed a general benefit from having worked as peer-student tutors. Conclusion: We found that employment as a peer-student tutor is beneficial for the respondents in general and for the acquisition and application of several CanMEDS roles in particular.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".