Policy and planning of prevention in Italy: Results from an appraisal of prevention plans developed by Regions for the period 2010–2012
Bibliographic record
Abstract
Health policies on disease prevention differ widely between countries. Studies suggest that different countries have much to learn from each other and that significant health gains could be achieved if all countries followed best practice. This paper describes the policy development and planning process relating to prevention activities in Italy, through a critical appraisal of Regional Prevention Plans (RPPs) drafted for the period 2010-2012. The analysis was performed using a specific evaluation tool developed by a Scientific Committee appointed by the Italian Ministry of Health. We appraised nineteen RPPs, comprising a total of 702 projects, most of them in the areas of universal prevention (62.9%) and prevention in high risk groups (27.0%). Italian Regions established prevention activities using an innovative combination of population and high-risk individuals approaches. However, some issues, such as the need to reduce health inequalities, were poorly addressed. The technical drafting of RPPs required some improvement; e.g. the evidence of the effectiveness and cost-effectiveness of the health interventions proposed was seldom reported. There were significant geographical differences across the Regions in the appraisal of RPPs. Our research suggests that continuous assessment of the planning process of prevention may become a very useful tool for monitoring, and ultimately strengthening, public health capacity in the field of prevention. Further research is needed to analyze determinants of regional variation.
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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 | 0.050 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".