Les obstacles juridico-politiques à la collaboration interprofessionnelle en santé et la nécessité de revoir certains modes de gouvernance prévus dans la LSSSS
Bibliographic record
Abstract
Interprofessional collaboration in healthcare has a major impact on quality, continuity and integration of services. While there is considerable scope for studying internal factors that enhance or restrict collaboration, few studies address systemic factors, such as legal and policy factors. This essay explores four types of barriers to interprofessional collaboration: the history of medical power in developing the foundations of the healthcare system, economic barriers such as suboptimal remuneration and inequity in the allocation of financial resources, barriers related to the regulatory framework of professions in Quebec, and obstacles related to administrative law. The author chooses to focus on particular modalities of governance of public institutions in the LSSSS, namely the council of physicians, dentists and pharmacists, the multidisciplinary council and the council of nurses, because these professional councils are a place of expression of professional leadership in public health institutions. As the overhaul of the LSSSS will take place in the near future, in continuity with « Bill 10 » of 2015, the author suggests new lines of inquiry to rethink the structure of these professional councils to foster interprofessional collaboration and integration of services, in order to improve access to health care.
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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 | 0.036 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.024 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 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".