Interprofessional Collaborative Practice and Law: A Reflective Analysis of 14 Regulation Structures
Bibliographic record
Abstract
Background: Interprofessional collaboration (IPC) is a key element of an efficienthealthcare system. Are healthcare systems structured to facilitate IPC?Methods and findings: Fourteen jurisdictions were chosen and researched usinglegal and social sciences databases. Generally, there was a lack of understandingof the legal principles in literature on policy and IPC. That aside, every jurisdic-tion had acts and regulation specific to health professions. There were numerouspathways to professional regulation and no clear consensus. Regarding IPC pres-ence in legal text, there were two main integration pathways: professional-basedand organization-based approaches.Conclusion: Although the practice of IPC is important, its presence in regulationis still discrete. If the aim is to strengthen IPC, there must be more socio-legalresearch to properly address and inform policymakers.
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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 | 0.073 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".