A design thinking approach to evaluating interprofessional education
Bibliographic record
Abstract
The complex challenge of evaluating the impact of interprofessional education (IPE) on patient and community health outcomes is well documented. Recently, at the Radcliffe Institute for Advanced Study in the United States, leaders in health professions education met to help generate a direction for future IPE evaluation research. Participants followed the stages of design thinking, a process for human-centred problem solving, to reach consensus on recommendations. The group concluded that future studies should focus on measuring an intermediate step between learning activities and patient outcomes. Specifically, knowing how IPE-prepared students and preceptors influence the organisational culture of a clinical site as well as how the culture of clinical sites influences learners' attitudes about collaborative practice will demonstrate the value of educational interventions. With a mixed methods approach and an appreciation for context, researchers will be able to identify the factors that foster effective collaborative practice and, by extension, promote patient-centred care.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".