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 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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".