Evaluating the CanMEDS collaborator role in residents through multi-source feedback
Bibliographic record
Abstract
Background/Objectives To improve evaluation methods in assessing the CanMEDS role of Collaborator through multi-source feedback using the Interprofessional Collaborator Assessment Rubric (ICAR). Methods Pilot study - To determine inter-rater reliability of the ICAR. Anaesthesia residents were assessed on daily interactions over a two-week period by attending physicians during normal learning encounters. Inter-rater reliability assessed through Fleiss' Kappa and internal reliability measured through Cronbach’s alpha. On-going Research – 360-degree evaluation of medical residents by physicians, nurses, and allied health professionals to determine inter-rater reliability of ICAR from multiple medical professionals. Twenty residents, four in five various medical teaching unit, will be evaluated after a four week rotation by their attending physician, nurses, and allied health professionals (physiotherapists, occupational therapists, social workers, pharmacists, dieticians, etc). Inter-rater reliability assessed through Fleiss' Kappa and internal reliability measured through Cronbach’s alpha. Results The pilot study offered both quantitative and qualitative data. Quantitatively, the ICAR was found to be internally consistent with a Cronbach's alpha value of 0.87 (> 0.7 is cited as significant). However, the inter-rater reliability was -0.089 where > 0.7 is cited as significant. Qualitatively, comments from evaluating physicians noted that there should be push toward multi-source feedback. Conclusions The pilot study results have allowed our research team to progress to our current, on-going, research. Although the ICAR is a internally reliable tool, it, and resident assessment, needs to tested under appropriate evaluation conditions including incorporating multiple raters (physicians, nurses, and allied health professionals) over extended (non-daily) observation periods.
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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 | 0.050 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".