Managing the gap between policy and practice through Intermediaries for Quality Improvement
Bibliographic record
Abstract
A gap exists between policymaking for quality improvement and the realization of these policies in practice. Using previous research on intermediaries, a conceptual model of an Intermediary for Quality Improvement (IQI) is developed. The model highlights the characteristics of structural positions, mediating approaches, and duration as a way to describe an IQI. The conceptual model is used to examine two cases in which Famna, the Swedish Association for Non-profit Health and Social Service Providers, has supported both policymaking and the implementation of policies at a provider level. The cases are the national strategy for quality improvement by open comparisons in health care and social services and a new regulation on quality management systems in health care and social services. Using the concept of an IQI deepens the understanding of how top-down and bottom-up perspectives may be managed to realize good quality of services.
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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 | 0.043 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".