Best practice portals in health promotion and disease prevention: approaches, definitions, and intervention evaluation criteria
Bibliographic record
Abstract
Introduction: The evaluation of practices is a valuable source of evidence in the context of an evidence-based approach to public health. Best practice portals (BPPs) are promising tools for facilitating access to recommended programmes, monitoring and improving the quality of interventions. There are several such portals in Europe, but there is little work in the scientific literature on the subject. The study aimed to identify and characterise BPPs in health promotion and disease prevention and analyse the approaches, definitions, and criteria for evaluating interventions. Methods: To identify portals, websites of public health institutions and organisations, the PubMed database and grey literature were searched. The material consisted of elements of each portal's design, information available on their websites, and collected publications. The study applied a qualitative analysis with a descriptive approach and covered a detailed description of the four selected portals. Results: Among the analysed BPPs, three were from the European region, and one was from Canada (pioneer in developing best practice tools). The dates of launching the portals ranged from the year 2003 to 2016. The number of interventions collected in the databases ranged from 120 to 337. Portals were useful, well-designed, and developed tools. BPPs differed in terms of their objectives and roles, adopted standards and criteria for assessing practices, and other operational factors. In each portal, interventions underwent a rigorous and multilevel assessment process conducted by independent experts in the field and based on intervention evaluation criteria. Generally, the analysed catalogues described similar issues, e.g., Selection of the issue addressed by the practice, Description of a particular element of the practice, Theoretical foundation, or Evaluation/Effectiveness. However, we identified both similarities and differences in the adopted terms (names of criteria) and their definitions. It was shown that sometimes the same criterion had different names depending on the catalogue. On the other hand, criteria with identical or similar names could be defined differently within the detailed thematic scope. Conclusion: The similarities and differences presented in this work can serve as a valuable starting point for designing such tools to support practice-based and evidence-based decision-making in health promotion and disease prevention.
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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 | 0.244 | 0.377 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.044 | 0.050 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".