Designing and Governing Responsive Local Care Systems – Insights from a Scoping Review of Paramedics in Integrated Models of Care
Bibliographic record
Abstract
Introduction: Programs that fill gaps in fractured health and social services in response to local needs can provide insight on enacting integrated care. Grassroots programs and the changing roles of paramedics within them were analyzed to explore how the health workforce, organizations and governance could support integrated care. Methods: A study was conducted following Arksey and O'Malley's method for scoping reviews, using Valentijn's Rainbow Model of Integrated Care as an organizing framework. Qualitative content analysis was done on clinical, professional, organizational, system, functional and normative aspects of integration. Common patterns, challenges and gaps were documented. Results: After literature search and screening, 137 documents with 108 unique programs were analysed. Paramedics bridge reactive and preventative care for a spectrum of population needs through partnerships with hospitals, social services, primary care and public health. Programs encountered challenges with role delineation, segregated organizations, regulation and tensions in professional norms. Discussion: Five concepts were identified for fostering integrated care in local systems: single point-of-entry care pathways; flexible and mobile workforce; geographically-based cross-cutting organizations; permissive regulation; and assessing system-level value. Conclusion: Integrated care may be supported by a generalist health workforce, through cross-cutting organizations that work across silos, and legislation that balances standardization with flexibility.
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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 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".