Examining the educational value of a CanMEDS roles framework in pediatric morbidity and mortality rounds
Bibliographic record
Abstract
BACKGROUND: In order to determine whether the CanMEDS roles could be helpful in solidifying knowledge during clinical training, we examined quality of care issues identified during morbidity and mortality (M&M) rounds. METHODS: During the M&M rounds, following the case presentation, there was a pause and attendees were asked to identify quality of care issues that were present in the case. The attendees were assigned to a CanMEDS prompted group or non-prompted group. Following the rounds, the issues were identified, coded according to CanMEDS role, and compared between groups. RESULTS: A total of 111 individuals identified a total of 350 issues; 57 individuals were in the CanMEDS-prompted group and 54 were in the unprompted group. The mean number of issues identified was significantly higher in the CanMEDS-prompted group compared to the unprompted group (3.7 versus 2.6, p = 0.039). There were significantly more issues raised in the prompted group for the roles of communicator, collaborator, scholar and professional. CONCLUSIONS: Using CanMEDS roles as prompts, attendees at M&M rounds identify more quality of care issues than if not given a prompt. Use of the CanMEDS framework may assist learners to consolidate the linkage between expected training objectives and the complexities of clinical practice.
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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 | 0.023 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".