Ten years of integrated care: backwards and forwards. The case of the province of Québec, Canada
Bibliographic record
Abstract
INTRODUCTION: Québec's rapidly growing elderly and chronically ill population represents a major challenge to its healthcare delivery system, attributable in part to the system's focus on acute care and fragmented delivery. DESCRIPTION OF POLICY PRACTICE: Over the past few years, reforms have been implemented at the provincial policy level to integrate hospital-based, nursing home, homecare and social services in 95 catchment areas. Recent organizational changes in primary care have also resulted in the implementation of family medicine groups and network clinics. Several localized initiatives were also developed to improve integration of care for older persons or persons with chronic diseases. CONCLUSION AND DISCUSSION: Québec has a history of integration of health and social services at the structural level. Recent evaluations of the current reform show that the care provided by various institutions in the healthcare system is becoming better integrated. The Québec health care system nevertheless continues to face three important challenges in its management of chronic diseases: implementing the reorganization of primary care, successfully integrating primary and secondary care at the clinical level, and developing effective governance and change management. Efforts should focus on strengthening primary care by implementing nurse practitioners, developing a shared information system, and achieving better collaboration between primary and secondary 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| 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".