How does interprofessional education affect attitudes towards interprofessional collaboration? A rapid realist synthesis
Bibliographic record
Abstract
Interprofessional collaboration (IPC) in healthcare is regarded as important by professionals, as it increases the quality of care while decreasing costs. Interprofessional education (IPE) is a prerequisite for IPC and influences learners' attitudes, knowledge, and collaboration skills. Since attitudes shape behavior, understanding how they are formed is crucial for influencing IPC in learners' professional practice. We investigated what kind of IPE works, for which students, how, and in what circumstances to develop positive attitudes towards IPC. Using realist synthesis, we extracted causal mechanisms that produce positive attitude outcomes and the conducive contexts that trigger them. Our analysis resulted in six plausible context-mechanism-outcome configurations that explain positive attitude development. Positive IPC attitudes are more likely to arise in contexts where IPE provides time and facilities for formal and informal interactions, as this allows learners to get to know each other both professionally and personally, fostering trust, respect, and mutual liking. Additionally, positive attitudes are more likely in contexts where the IPE curriculum is perceived as career-relevant and boosts confidence. Key mechanisms of positive attitude development include getting to know the other learners professionally and personally, experiencing positive affect during IPE, and learners experiencing mutual dependence. Sustained positive attitudes are more likely to develop when there is organizational support for IPC and professionals attend IPE on an ongoing basis, allowing the attitudes and values expected in IPC to be positively reinforced and eventually integrated into the learners' personal value system.
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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 | 0.019 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".