Building partnerships: A case study of physical activity researchers and practitioners collaborating to build evidence to inform the delivery of a workplace step count challenge
Bibliographic record
Abstract
Background: Walking is an integral part of Scotland's National Physical Activity Strategy, and the charity Paths for All's Workplace Step Count Challenge is a flagship programme within this strategy to promote physical activity. Effectively promoting physical activity requires collaborative engagement between stakeholders. However, there is limited guidance on how to do this. The aim of this case study is to share an example of a partnership between Paths for All and researchers to inform the development and delivery of the Workplace Step Count Challenge. Method: An overview of the partnership, example activities, reflections on opportunities and challenges, and suggestions for future partnership working are considered. Results: The partnership has evolved and strengthened over time through building trust. Many of the research activities provide an evidence base for the intervention. This work is mutually beneficial providing support for the work of the organisation, and opportunities for researchers to undertake "real world" research, leading to formal outputs and funding. The "real world" nature is challenging to integrate the most robust research designs. Recommendations for developing future partnerships were identified. Conclusion: Promoting physical activity effectively requires partnership working, and this paper provides insight into how such partnerships can work to inform future collaborations.
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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 | 0.046 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.041 | 0.014 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.009 | 0.013 |
| 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".