“Learning from the past, building for the future”: Participatory approaches to evaluation of the REsearch into POlicy to enhance Physical Activity (REPOPA) project
Bibliographic record
Abstract
Issue/Problem REPOPA, a five-year European Commission (EC) funded project, is a multi-country collaboration on approaches to describe and enhance the use of research in physical activity policy in Europe. This presentation will describe the participatory approach and theoretical underpinnings used to develop an evaluation framework for this project. Description of the Problem The challenges faced in the evaluation design included balancing formative and summative indicators; adapting evaluation to the different policy, institutional, and stakeholder contexts across countries; and developing an approach appropriate to an evolving research project. The evaluation aims to meet EC requirements for assessment of project activities and impact and strengthen internal Consortium processes for decision-making, management, and research implementation. Results (effects/changes) A literature review and consultation process with REPOPA consortium members was conducted to adapt the “Knowledge to Action” cycle and the RE-AIM framework for use in the evaluation of REPOPA aims. Interviews with representatives of each partner country (n = 6) were conducted at two points in time to inform initial evaluation framework development. Refinement occurred through feedback at two consortium meetings. The application and adaptation of relevant theories and the participatory engagement of consortium members informed the development of common operating principles across work packages (WP) and related indicators. The final evaluation framework includes 12 areas for internal and external evaluation and a core set of 34 process and outcome indicators that are common across six WPs and also WP-specific indicators. The indicators allow for cross-WP and within-WP data collection. Internal monitoring reports will ensure continuous improvement during project implementation. Indicators were designed to address, but also complement standard EC reporting requirements. Lessons learned (1) The practice of evaluation in a multi-year, multi-country research project is enhanced through a participatory approach from the outset; (2) Continuous improvement in project implementation and management is enabled through timely collection and feedback of evaluation data to the team; and, (3) Pragmatic proxy indicators and clear project logic are necessary when evaluating policy and research relationships. Key messages It is critical to share learnings from innovative approaches to evaluating interventions aimed at linking evidence and actions– Evaluation of multi-country collaboration and programmes of research will allow Europe to “learn from the past and build for the future”
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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 | 0.291 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".