Interprofessional Education and Practice Guide No. 3: Evaluating interprofessional education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it