Interprofessional Education and Practice Guide No. 3: Evaluating interprofessional education
Bibliographic record
Abstract
We have witnessed an ongoing increase in the publication of evaluation work aimed at measuring the processes and outcomes related to a range of interprofessional education (IPE) activities and initiatives. Systematic reviews of IPE have, however, suggested that while the quality of evaluation studies is improving, there continues to be a number of empirical weaknesses with this work. In an effort to enhance the quality of IPE evaluation studies, this guide provides a series of ideas and suggestions about how to undertake a robust evaluation of an IPE event. The guide presents a series of key lessons for colleagues to help them undertake a good quality IPE evaluation, covering a range of methodological, practical and ethical issues. These include: the formation of evaluation questions, use of evaluation models and theoretical perspectives, advice about the selection of qualitative, quantitative and mixed methods evaluation designs, managing evaluation resources, and ideas about disseminating evaluation results to the broader IPE community. It is anticipated that this guide will assist IPE colleagues in undertaking high-quality evaluation in order to provide valuable evidence for different stakeholders, and also help inform the scholarly knowledge for the interprofessional field.
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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 | 0.043 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".