Assessment of continuing interprofessional education: Lessons learned
Bibliographic record
Abstract
Although interprofessional education (IPE) and continuing interprofessional education (CIPE) are becoming established activities within the education of health professions, assessment of learners continues to be limited. Arguably, this in part is due to a lack of IPE and CIPE within in the clinical workplace. The accountability of interprofessional teams has been driven by quality assurance and patient safety, though sound assessment of these activities has not yet been achieved. The barriers to team assessment in CIPE appear related to access and resources. Simulated team training and assessment are expensive, and because of staffing shortages, learning in clinical practice is often the only way forward, but is obviously not ideal. Despite these difficulties, the principles of assessment should be adhered to in any CIPE program. This article explores key issues related to the assessment of CIPE. It reflects on processes of designing and introducing an IPE activity into an existing university curriculum and focuses on determining the purpose of the assessment and the use of collaborative competencies to help determine assessment. The article also discusses the use of an assessment blueprint to ensure that learners are exposed to the relevant collaborative competencies. In addition, the article discusses the use of multiple assessment methods and the potential of simulation in the assessment of CIPE.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".