Interprofessional collaboration and health policy: results from a Quebec mixed method legal research
Bibliographic record
Abstract
Interprofessional collaboration (IPC) is central to effective care. This practice is structured by an array of laws, regulations and policies but the literature on their impact on IPC is scarce. This study aims to illustrate the gap between the texts and clinicians’ knowledge of the legal framework using an anonymous web-based survey. The survey, sent to nurses and physicians in Quebec, Canada, focused on the IPC legal framework, legal knowledge sources and IPC perceptions or beliefs. The primary outcome was to determine the gap between the law and understanding of the law. The secondary outcome was to identify legal knowledge sources for clinicians in Quebec. A total of 267 participants filled in the survey. For knowledge acquisition, 40% of physicians turned to insurers whereas 43% of nurses turned to their regulatory body. Only 30% of physicians correctly identified what activity is reserved for physicians while 39% of nurses correctly identified their reserved activity. Regarding legal perceptions, 28% of physicians and 39% of nurses thought IPC could increase their liability. These participants have a higher tendency to name liability-related issues as barriers to IPC. These results show an important discrepancy between clinicians’ knowledge about law and policies, and the actual texts themselves. This gap can lead to misinterpretations of the law by clinicians, ineffective policy changes by policymakers and can perpetuate ineffective implementation of IPC.
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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 | 0.015 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".