Improving Accessibility to Services and Increasing Efficiency Through Merger and Centralization in Québec
Bibliographic record
Abstract
On 25 September 2014, Bill 10 was tabled to reorganize Québec's health and social services network through the abolition of an administrative layer at the regional health authority level and institutional mergers of health and social services facilities under a new governance structure.Thus, the province's 182 health and social services facilities were merged into 34 Centre intégré de santé et des services sociaux (CISSS) / Centre intégré universitaire de santé et des services sociaux (CIUSSS).CISSS/CIUSSS are responsible for delivering a range of health and social services in a designated territory through the administrative integration of facilities including: local community health centres, generalized and specialized hospitals, psychiatric hospitals, child and youth protection centres, residential and longterm care centres, and rehabilitation centres.These mergers were operationalized notably by a new governance structure whereby the minister-appointed board of directors in each CISSS/CIUSSS reports directly to the Minister of Health and Social Services.As such, a centralization of powers was also achieved.While formal evaluations of reform performance have yet to be completed, analyses projecting potential difficulties of the reform were presented during special consultation hearings.Among the key concerns identified was whether there was evidence to support claims that administrative mergers increased efficiency by achieving economies of scale.Additionally, implicit to Bill 10 is the assumption that continuity of care will follow from administrative mergers.Strategic mergers through professional networks can promote more streamlined approaches to information sharing.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".