Enhancing policy implementation to improve healthcare practices: The role and strategies of hybrid national‐local support structures
Bibliographic record
Abstract
BACKGROUND: In this study, we followed a national initiative to enhance the use of quality indicators gathered in national quality registries (NQRs) for improvement of clinical practices in Swedish healthcare, more specifically by investigating the support strategies of regional support centers with national and local missions. The aim was to increase knowledge on the role, challenges, and strategies of support structures with mixed and complex missions in the healthcare system. METHODS: Documents and 25 semistructured interviews with staff at 6 regional support centers, ie, quality registry centers, formed this multiple case study. Data were analyzed using conventional content analysis. RESULTS: The centers' strategies varied from developing the NQRs to become more suitable for improvement to supporting healthcare's use of NQRs, from the use of task to process-oriented support strategies, and from taking on national responsibilities to responding to local initiatives. All quality registry centers engaged in initiatives inspired by the Breakthrough Series approach. Some used preexisting change concepts or collaborated with local development units. A main challenge was to overcome a lack of formal mandate to act in the healthcare organizations they served. CONCLUSIONS: Support functions with mixed and complex missions have to use a variation of strategies to reach relevant actors and achieve changes. This study provides valuable input for policy and decision-makers on the support strategies used and challenges of support functions with complex missions situated in-between national and local levels of the healthcare system, here denoted hybrid national-local support structures.
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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 | 0.040 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| 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".