Exploring the effectiveness of a cascading mentorship model in developing CanMEDS competencies in postgraduate medical education: a qualitative interview study among resident mentors at a medical school in Canada
Bibliographic record
Abstract
OBJECTIVES: The CanMEDS framework, an educational framework for physicians used in Canada, defined competencies that physicians require to meet patients' needs, all of which can be cultivated through mentorship activities. The Advocacy Mentorship Initiative (AMI) at the University of Toronto used a cascading mentorship model (CMM), whereby resident mentors (RMs) mentored undergraduate medical student mentors (MSMs), who in turn mentored youth raised in at-risk environments. Both RMs and MSMs were mentored by the AMI programme lead, a staff psychiatrist, with expertise in child and adolescent psychiatry. The research question of this study was as follows: What were the merits of using a CMM in enhancing the knowledge, competencies and residency experiences of RMs in AMI? DESIGN: Qualitative interview study. SETTING AND PARTICIPANTS: RMs involved in AMI from January 2017 to December 2020 were invited to participate in the study. A total of 11 RMs agreed to participate. METHODS: Interviews were conducted to canvas participants about how AMI impacted them, and these were recorded, transcribed and anonymised. Braun and Clarke's approach to thematic analysis was used to identify 'subthemes' and 'themes'. RESULTS: Eleven RMs participated in the study. A major theme identified was how AMI enhanced the medical learner experience by augmenting the educational experience of MSMs, strengthening RMs' values and attitudes, and strengthening RMs' knowledge and competencies. The second theme captured was the effective facets of a mentorship programme in AMI, including the CMM, and collaborative and inclusive relationships between mentors and mentees. CONCLUSIONS: RMs identified that the CMM of AMI cultivated CanMEDS competencies in medical learners; deepened medical learners' understanding of social determinants of health; and offered a bidirectional approach to teaching and learning between MSMs and RMs. MSMs and RMs also learnt from the staff psychiatrist.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".