Taking stock of the evidence – from data use to health system improvement
Bibliographic record
Abstract
Many countries are benefiting from the linkage and analysis of personal health data to provide the evidence needed for health policy decisions to improve the quality and efficiency of health care. Examples range from reporting on the cost-effectiveness and clinical appropriateness of care in Finland, Korea and Singapore; to assessments of the quality and efficiency of clinical guidelines in Sweden; to evaluating the safety of patient screening in Germany; to evaluating the quality of surgical outcomes in Israel and the United Kingdom; to examining care transitions in Australia and Canada.This chapter summarises 29 within-country projects and 10 multi-country projects deemed by country respondents to be policy relevant and to exemplify good practices in data protection. Among them, 14 study leaders were interviewed to provide additional information about their project and its relevance to health policy, as well as the steps taken to ensure privacy-respectful data use. For these 14 projects, a detailed case study summary is presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".