Improving the clarity of the interprofessional field: Implications for research and continuing interprofessional education
Bibliographic record
Abstract
Significant investments are being made around the world to improve interprofessional collaboration, yet limits in our knowledge of this field restrict the ability of decision makers to base their decisions upon evidence. Clarity of the interprofessional field is blurred by a conceptual and semantic confusion that affects our understanding of key elements of education and practice activities, their interlinked relationship, and their effects on health or system outcomes. Systematic reviews of interprofessional education (IPE) and interprofessional collaboration (IPC) have provided some insight into the nature and effectiveness of this field, but a lack of clarity remains. In this article we report on a scoping review currently being undertaken to analyze the interprofessional field, improve its conceptual clarity, and identify elements needed to enhance its development. Emerging review findings regarding participants and settings, interventions, and outcomes are reported. The article provides implications from this review and discusses them in relation to continuing IPE and future research.
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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 | 0.278 | 0.442 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.017 | 0.034 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".