Reliability of the Interprofessional Collaborator Assessment Rubric (ICAR) in Multi Source Feedback (MSF) with post-graduate medical residents
Bibliographic record
Abstract
BACKGROUND: Increased attention on collaboration and teamwork competency development in medical education has raised the need for valid and reliable approaches to the assessment of collaboration competencies in post-graduate medical education. The purpose of this study was to evaluate the reliability of a modified Interprofessional Collaborator Assessment Rubric (ICAR) in a multi-source feedback (MSF) process for assessing post-graduate medical residents' collaborator competencies. METHODS: Post-graduate medical residents (n = 16) received ICAR assessments from three different rater groups (physicians, nurses and allied health professionals) over a four-week rotation. Internal consistency, inter-rater reliability, inter-group differences and relationship between rater characteristics and ICAR scores were analyzed using Cronbach's alpha, one-way and two-way repeated measures ANOVA, and logistic regression. RESULTS: Missing data decreased from 13.1% using daily assessments to 8.8% utilizing an MSF process, p = .032. High internal consistency measures were demonstrated for overall ICAR scores (α = .981) and individual assessment domains within the ICAR (α = .881 to .963). There were no significant differences between scores of physician, nurse, and allied health raters on collaborator competencies (F2,5 = 1.225, p = .297, η2 = .016). Rater gender was the only significant factor influencing scores with female raters scoring residents significantly lower than male raters (6.12 v. 6.82; F1,5 = 7.184, p = .008, η 2 = .045). CONCLUSION: The study findings suggest that the use of the modified ICAR in a MSF assessment process could be a feasible and reliable 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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; a candidate call from one teacher head, 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".