The Evolution of Primary Health Care Teams and Integrated Health Services Delivery in Four Canadian Provinces
Bibliographic record
Abstract
Improving integrated health services for patients with two or more chronic illnesses is a priority in Canada as our health systems grapple with their complex needs and the services they require (Kirst et al. 2017; Suter et al. 2014). Team-based primary health care (PHC) models have been implemented in diverse ways to improve patient experience and to bet- ter coordinate integrated care to improve population health and reduce the cost of health care (Kirst et al. 2017; Buljac-Samardzic et al. 2010). The structure and composition of interprofessional primary health care (IPHC) models vary across provinces; however, their common goal is to address the four elements of the Quadruple Aim (population health, patient experience, provider experience, and reducing costs) (Bodenheimer and Sinsky 2014). Although research exists on interprofessional teams and health service integration, understanding the effectiveness of the development and implementation of team-based models for patients with two or more chronic illnesses has been challenging. Policymakers, decision-makers, providers, and patient groups have little evidence on what policies and structures facilitate, incentivize, or prevent integrated service delivery, especially for patients with complex needs. This knowledge gap has had an impact on the reform of service integration for patients with complex needs through IPHC teams. A policy analysis was conducted in four Canadian provinces to examine the policies and structures that scaffold such reform, identifying barriers and facilitators to the implementation of PHC teams and integrated health services. This study was carried out in British Columbia (BC), Alberta (AB), Ontario (ON), and Québec (QC) to understand different models implemented in these provinces and to ensure representation of east, west, and central Canada.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".