Does Integrated Care Carry the Gene of Bureaucracy? Lessons from the Case of Québec
Bibliographic record
Abstract
INTRODUCTION: Demographic and epidemiological transitions of industralized countries mean health systems have to integrate health and social services to respond to the changing needs of their populations. Efforts to integrate care involve important policy and structural changes. This paper examines whether integration efforts are lost in translation during the bureaucratic appropriation of models, or, in an allegorical way, do they reveal genes of bureaucracy? DESCRIPTION: Since the 1960s, the health system of Québec has undergone four major structural and progressively integrative transformations, characterized as - modernization, shock of reality, explicit integration, and centralization phases. DISCUSSION: Although integration efforts progressively transformed Québec's health and social services system, embedded bureaucracies impeded the realisation of these projects. Notably, inadequate change management strategies and lack of integrated funding models hindered integration efforts. Furthermore, there was variability in government prioritisation and support of different aspects of the model by making some components happen, helping others happen and letting others happen. CONCLUSION: Drawing insights from bureaucratic obstacles to integration efforts may improve implementation strategies. This paper highlights important policy and administrative challenges that have to be taken into consideration in improving the implementation of integrated care initiatives in a real-life context.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".