Development and validation of the interprofessional collaborator assessment rubric ((ICAR))
Bibliographic record
Abstract
There have been increasing calls for a competency-based approach in interprofessional education (IPE). The purpose of this multi-site research project was to develop a validated set of interprofessional collaborator competencies and an associated competency-based assessment rubric, in both English and French languages. The first phase involved a detailed comparative analysis of peer-reviewed and grey literature using typological analysis to construct a draft list of interprofessional collaborator competency categories and statements. A two-round Delphi survey of experts was undertaken to validate these competencies. In the second phase, an assessment rubric was developed based on the validated competencies and then evaluated for utility, clarity, practicality and fairness through multi-site focus groups with students and faculty at both college and university levels. The paper outlines an approach to developing, constructing and validating a bilingual instrument for interprofessional learning and assessment. The approach was collaborative in nature, involving an interprofessional project team and respondents from across multiple health profession education programs. The Delphi survey ratings indicate a high level of agreement with the importance of the competency statements and focus group participants rated the rubric positively and felt it had value. The focus group results were also useful in pre-piloting the contextual application of the instrument across multiple health profession education programs. This rubric instrument may be used across a variety of professions and learning contexts. Future work includes evaluation of further dimensions of validity and reliability for this tool across a variety of settings.
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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 | 0.117 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".