Development of measurable indicators to enhance public health evidence-informed policy-making
Bibliographic record
Abstract
BACKGROUND: Ensuring health policies are informed by evidence still remains a challenge despite efforts devoted to this aim. Several tools and approaches aimed at fostering evidence-informed policy-making (EIPM) have been developed, yet there is a lack of availability of indicators specifically devoted to assess and support EIPM. The present study aims to overcome this by building a set of measurable indicators for EIPM intended to infer if and to what extent health-related policies are, or are expected to be, evidence-informed for the purposes of policy planning as well as formative and summative evaluations. METHODS: The indicators for EIPM were developed and validated at international level by means of a two-round internet-based Delphi study conducted within the European project 'REsearch into POlicy to enhance Physical Activity' (REPOPA). A total of 82 researchers and policy-makers from the six European countries (Denmark, Finland, Italy, the Netherlands, Romania, the United Kingdom) involved in the project and international organisations were asked to evaluate the relevance and feasibility of an initial set of 23 indicators developed by REPOPA researchers on the basis of literature and knowledge gathered from the previous phases of the project, and to propose new indicators. RESULTS: The first Delphi round led to the validation of 14 initial indicators and to the development of 8 additional indicators based on panellists' suggestions; the second round led to the validation of a further 11 indicators, including 6 proposed by panellists, and to the rejection of 6 indicators. A total of 25 indicators were validated, covering EIPM issues related to human resources, documentation, participation and monitoring, and stressing different levels of knowledge exchange and involvement of researchers and other stakeholders in policy development and evaluation. CONCLUSION: The study overcame the lack of availability of indicators to assess if and to what extent policies are realised in an evidence-informed manner thanks to the active contribution of researchers and policy-makers. These indicators are intended to become a shared resource usable by policy-makers, researchers and other stakeholders, with a crucial impact on fostering the development of policies informed by evidence.
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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 | 0.363 | 0.448 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.030 | 0.024 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".