Evaluating the implementation of community engagement guidelines (EVALUA GPS project): a study protocol
Bibliographic record
Abstract
INTRODUCTION: The EVALUA GPS project aims to evaluate the impact of the implementation of the National Institute for Health Care and Excellence (NICE) guideline 'Community engagement: improving health and well-being and reducing health inequalities' adapted to the Spanish context. METHODS AND ANALYSIS: Phase I: A tool will be designed to evaluate the impact of implementing the recommendations of the adapted NICE guideline. The tool will be developed through a review of the literature on implementation of public health guidelines between 2000 and 2021 and an expert's panel consensus. PHASE II: The developed tool will be implemented in 16 community-based programmes, acting as intervention sites, and 4 controls through a quasi-experimental pre-post study. Phase III: A final online web tool, based on all previously collected information, will be developed to support the implementation of the adapted NICE guidelines recommendations in other contexts and programmes. DATA COLLECTION AND ANALYSIS: Data will be collected through surveys and semistructured interviews. Quantitative and qualitative data will be analysed to identify implementation scenarios, changes in community engagement approaches, and barriers and facilitators to the implementation of the recommendations. All this information will be further synthesised to develop the online tool. ETHICS AND DISSEMINATION: The proposed research has been approved by the Clinical Research Ethics Committee of Aragon. Results will be presented at national and international conferences and published in peer-reviewed open access journals. The interactive online tool (phase III) will include examples of its application from the fieldwork.
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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 | 0.145 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.050 | 0.014 |
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".