Interprofessional education and collaborative practice policies and law: an international review and reflective questions
Bibliographic record
Abstract
BACKGROUND: Healthcare is a complex sociolegal setting due to the number of policymakers, levels of governance and importance of policy interdependence. As a desirable care approach, collaborative practice (referred to as interprofessional education and collaborative practice (IPECP)) is influenced by this complex policy environment from the beginning of professionals' education to their initiation of practice in healthcare settings. MAIN BODY: Although data are available on the influence of policy and law on IPECP, published articles have tended to focus on a single aspect of policy or law, leading to the development of an interesting but incomplete picture. Through the use of two conceptual models and real-world examples, this review article allows IPECP promoters to identify policy issues that must be addressed to foster IPECP. Using a global approach, this article aims to foster reflection among promoters and stakeholders of IPECP on the global policy and law environment that influences IPECP implementation. CONCLUSION: IPECP champions and stakeholders should be aware of the global policy and legal environment influencing the behaviors of healthcare workers to ensure the success of IPECP implementation.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".