Translational Formative Evaluation before Scale-up of a Physical Activity Intervention for Older Men
Bibliographic record
Abstract
ABSTRACT Introduction Despite irrefutable health benefits of physical activity, older adults remain among the least active Canadians. To achieve population health, physical activity interventions that proved effective in controlled research settings must be delivered at scale to reach broader populations of older adults across multiple settings. Formative evaluations are essential, as they identify barriers and enablers to implementation across levels of stakeholder groups and settings. Thus, we conducted a formative evaluation of a choice- and evidence-based physical activity intervention (Men on the Move) designed for scalability. Methods We adopted key elements of two implementation frameworks that place characteristics of the innovation, prevention delivery system, prevention support system, and prevention synthesis and translation system at the core of implementation success. Guided by the Interactive Systems Framework for Dissemination and Implementation, data were collected from delivery partners, including 1 leader from a key provincial recreation organization, 6 recreation directors/coordinators and 3 activity coaches, and 14 participants (older men). This research team participated in prevention support and prevention synthesis and translation systems. Two trained interviewers conducted telephone interviews with delivery partners, and five trained interviewers and a notetaker conducted in-person interviews with participants. Results Five themes emerged from analyses of delivery partner interviews: support, activity coaches, intervention delivery, Men on the Move continuation, and the absence of men. Two themes emerged from our analyses of participant data: monitoring and connectedness. Conclusion Lessons learned from this formative evaluation will guide the adaptation of the intervention to context and population for scale-up across British Columbia, Canada. In so doing, we aim to bridge the know–do–scale-up gap, which is imperative as we seek to improve older adult health at the population level.
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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 | 0.191 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".