Comparative case study of Quality Improvement Support Agencies (QISAs) as a systemic quality strategy: France and Québec
Bibliographic record
Abstract
In a high-quality health and social services system, policymakers encourage action at all levels of change to ensure the consistency of quality strategies, especially at the systemic level, in which the other three levels of change are nested and the whole system is structured. This study aims to present an analysis of Quality Improvement Support Agencies (QISAs) as a systemic strategy, which has been successfully implemented in several jurisdictions (countries and states). A comparative study of two critical cases in two different jurisdictions was carried out: Haute autorité de santé (HAS) in France and Institut national d’excellence en santé et services sociaux (INESSS) in Québec, Canada. Several sources of iteratively collected data were coded using a systematized approach. All data were processed and analyzed confidentially using the software QDA Miner 6.0.2. The results showed that HAS has a wider range of activities and INESSS has a narrower range. Their statutes of autonomy differ, with the former QISA more independent of the public power of its jurisdiction and the latter more at the service of public power. Though their products differ, these QISAs each have different effects (proximal, intermediate, and ultimate) on quality improvement in practice settings. Furthermore, it appears that they have each faced dilemmas in achieving systemic quality improvement. This study may inspire other jurisdictions to implement similar systemic quality improvement strategies or strengthen them if they have already been implemented.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".