The contribution of theory to the design, delivery, and evaluation of interprofessional curricula: BEME Guide No. 49
Bibliographic record
Abstract
Background: Interprofessional curricula have often lacked explicit reference to theory despite calls for a more theoretically informed field that illuminates curricular assumptions and justifies curricular practices. Aim: To review the contributions of theory to the design, delivery, and evaluation of interprofessional curricula. Methods: Four databases were searched (1988–2015). Studies demonstrating explicit and a high-quality contribution of theory to the design, delivery or evaluation of interprofessional curricula were included. Data were extracted against a comprehensive framework of curricular activities and a narrative synthesis undertaken. Results: Ninety-one studies met the inclusion criteria. The majority of studies (86%) originated from the UK, USA, and Canada. Theories most commonly underpinned “learning activities” (47%) and “evaluation” (54%). Theories of reflective learning, identity formation, and contact hypothesis dominated the field though there are many examples of innovative theoretical contributions. Conclusions: Theories contribute considerably to the interprofessional field, though many curricular elements remain under-theorized. The literature offers no “gold standard” theory for interprofessional curricula; rather theoretical selection is contingent upon the curricular component to which theory is to be applied. Theories contributed to interprofessional curricula by explaining, predicting, organizing or illuminating social processes embedded in interprofessional curricular assumptions. This review provides guidance how theory might be robustly and appropriately deployed in the design, delivery, and evaluation of interprofessional curricula.
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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 | 0.057 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".