Developing a Recursive Evaluation Plan of a Complex Interprofessional Healthcare Education Initiative
Bibliographic record
Abstract
Background: A university interprofessional education (IPE) and interprofessional practice (IPP) initiative is a complex undertaking: incorporating multiple system levels (administration, faculty, students, patients), integrating many theoretical perspectives, and coordinating a host of individual IPE research projects. Guidance for evaluating such an IPE initiative is lacking.Methods and Findings: We describe five key challenges to evaluating the effectiveness of such an initiative, and the processes and tools we have developed to meet those challenges. We draw from recent developments in evaluation science to theoretically ground our description. Additionally, we share concrete tools we have developed in the process. By tacking between theoretical and concrete aspects of our efforts, we hope to both provide ideas for other IPE initiatives, as well as provide a basis for future research comparing cases (complex university IPE initiatives).Conclusions: While all complex IPE university initiatives are unique, we suspect that they share many common evaluation challenges. By framing these common practical challenges as common theoretical challenges, we seek to offer a description of our concrete case as well as a basis for future comparison of similar initiatives.
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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 | 0.114 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".