Evolution of Experience of Care of Patients with and without Chronic Diseases following a Québec Primary Healthcare Reform
Bibliographic record
Abstract
Objectives. To assess the extent to which new primary healthcare (PHC) models implemented in two regions of Quebec have improved patient experience of care, unmet needs, and use of services for individuals with and without chronic diseases, compared with other forms of PHC practices. Methods. In 2005 and 2010, we carried out population and organization surveys. We divided PHC organizations into new model practices and other practices and followed the evolution over time of patient experience of care. Results. Patients with chronic diseases had better accessibility but worse continuity of care in the new model practices than in the other practices at both time periods. Through the reform, accessibility decreased evenly in both groups, but continuity and perceived outcomes improved more in the other practices. Use of primary care services decreased more in the new model practices. Among patients without chronic disease, accessibility decreased much less in the new models and responsiveness increased more. There was no significant change in ER attendance and hospitalization. Conclusion. The evolution of patient experience of care has been more favorable for patients without chronic diseases. These findings raise concerns about equity since the aim of the PHC reform was targeting in priority individuals with the greatest needs.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".