Making a move in exercise referral: co-development of a physical activity referral scheme
Bibliographic record
Abstract
Background: Translational research is required to ensure exercise referral schemes (ERSs) are evidence-based and reflect local needs. This article reports process data from the co-development phase of an ERS, providing an insight into (i) factors that must be considered when translating evidence to practice in an ERS setting, and (ii) challenges and facilitators of conducting participatory research involving multiple stakeholders. Methods: An ERS was iteratively co-developed by a multidisciplinary stakeholder group (commissioners, managers, practitioners, patients and academics) via five participatory meetings and an online survey. Audio data (e.g. group discussions) and visual data (e.g. whiteboard notes) were recorded and analysed using NVivo-10 electronic software. Results: Factors to consider when translating evidence to practice in an ERS setting included (i) current ERS culture; (ii) skills, safety and accountability; and (iii) resources and capacity. The co-development process was facilitated by needs-analysis, open questions, multidisciplinary debate and reflective practice. Challenges included contrasting views, irregular attendance and (mis)perceptions of evaluation. Conclusion: The multidisciplinary co-development process highlighted cultural and pragmatic issues related to exercise referral provision, resulting in an evidence-based intervention framework designed to be implemented within existing infrastructures. Further work is required to establish the feasibility and effectiveness of the co-developed intervention in practice.
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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 | 0.120 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".