Innovation in Healthcare, Innovation in Law: Does the Law Support Interprofessional Collaboration in Canadian Health Systems?
Bibliographic record
Abstract
Interprofessional collaboration in health care describes a model of practice in which multiple health professionals work together in a team-based approach to patient care. A growing body of literature demonstrates that interprofessional collaboration advances health care quality and safety, improves patient outcomes and experiences of care, and promotes job satisfaction among health professionals. Governments and health organizations across Canada are working to advance interprofessional health care delivery. This article examines the importance of law in supporting a shift to interprofessional collaboration in Canadian health care and discusses two key aspects of the legal context in which health practitioners work. First, the article discusses trends in the legal regulation of health professions in Canada, including law reform initiatives aimed at promoting collaborative practice and at expanding scopes of practice to break down the historically siloed approach to health care delivery. Second, the article examines civil liability rules that courts apply when allegations of negligence are made against health care providers working in team-based situations. regarding responsibility for patient care and outcomes. The article illustrates how legal innovations, such as new models of health profession regulation and legal adaptability through judicial understanding of the modern context of health service delivery, are important to the advancement of interprofessional collaboration in Canadian health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.050 | 0.030 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".