Improving evaluation of the CanMEDS collaborator role:reliability of the Interprofessional Collaborator Assessment Rubric (ICAR) andgender bias in multi-source feedback
Bibliographic record
Abstract
Since the inception of the Royal College of Physicians and Surgeons of Canada (RCPSC) CanMEDS framework, there has been inequality between the assessment of the Medical Expert role and the six non-Medical Expert roles. The purpose of the study was to evaluate the reliability of the use of the Interprofessional Collaborator Assessment Rubric (ICAR) in a multi-source feedback (MSF) approach for assessing post-graduate medical residents’ CanMEDS Collaborator competencies. A secondary investigation attempted to determine whether characteristics of raters (i.e., experience, gender, or frequency of interaction with resident) had any influence on overall ICAR score. The ICAR is a 17- item (and global score) assessment tool utilizing a 9-point scale and two open-text responses. The study involved medical residents receiving ICAR assessments from three (3) rater groups (physicians, nurses, and allied health professionals) over a single fourweek rotation. Residents were recruited from four (4) unique medical disciplines. Of those participating residents, sixteen (16) residents were randomly chosen. Six (6) of those received at least two (2) assessments from each rater group and were included in the analysis. All nurses and allied health professionals in participating medical / surgical units were invited to participate and were excluded from analysis if they were absent for at least one week of normal shift work or explicitly stated they did not interact with resident. Physicians were self-appointed by the residents. Statistical analysis utilized Cronbach’s alpha, compared overall ICAR scores using one-way and two-way, repeated measures ANOVA, and logistic regression. Missing data using a single imputation stochastic regression method and was compared to the missing data from a pilot study using pair-sample t-test. Results revealed a high response rate (76.2%) with a statistically significant difference between the gender distributions in each rater group, male physicians (81.8%), female nurses (92.5%), and female allied health professionals (88.4%), p < .001. Missing data decreased from 13.1% using daily assessments to 8.8% utilizing an MSF process, p = .032. An overall Cronbach’s alpha coefficient of α = .981 revealed high internal consistency reliability. Each ICAR domain also demonstrated high internal consistency, ranging between .881 - .963. The profession of the rater yielded no significant effect with a very small effect size (F₂,₅ = 1.225, p = .297, η² = .016). The only significant, main-effect on overall ICAR score was found to the gender of the rater (F₁,₅ = 7.184, p = .008, η² = .045). Female raters scored residents significantly lower than male raters (6.12 v. 6.82). Logistic regression analysis revealed that male raters were 3.08 times more likely than female raters to provide an overall ICAR score of above 6.0 (p = .013) and 3.28 times more likely to score above 7.0 (p = .005). A significant interaction effect resulted from a two-way repeated measures ANOVA analysis involving the frequency of interaction between raters and residents across items (F = 2.103, p = .025, η² = .014). The study findings suggest that the use of the modified ICAR form in a MSF assessment process could be a feasible assessment approach to providing formative feedback to post-graduate medical residents on Collaborator competencies.
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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 | 0.273 | 0.417 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".