Factors that influence implementation at scale of a community-based health promotion intervention for older adults
Bibliographic record
Abstract
BACKGROUND: Despite the many known benefits of physical activity (PA), relatively few older adults are active on a regular basis. Older adult PA interventions delivered in controlled settings showed promising results. However, to achieve population level health impact, programs must be effectively scaled-up, and few interventions have achieved this. To effectively scale-up it is essential to identify contextual factors that facilitate or impede implementation at scale. Our aim is to describe factors that influence implementation at scale of a health promotion intervention for older adults (Choose to Move). This implementation evaluation complements our previously published study that assessed the impact of Choose to Move on older adult health indicators. METHODS: To describe factors that influenced implementation our evaluation targeted five distinct levels across a socioecological continuum. Four members of our project team conducted semi-structured interviews by telephone with 1) leaders of delivery partner organizations (n = 13) 2) recreation managers (n = 6), recreation coordinators (n = 27), activity coaches (n = 36) and participants (n = 42) [August 2015 - April 2017]. Interviews were audio-recorded and professionally transcribed and data were analyzed using framework analysis. RESULTS: Partners agreed on the timeliness and need for scaled-up evidence-based health promotion programs for older adults. Choose to Move aligned with organizational priorities, visions and strategic directions and was deemed easy to deliver, flexible and adaptable. Partners also noted the critical role played by our project team as the support unit. However, partners noted availability of financial resources as a potential barrier to sustainability. CONCLUSIONS: Even relatively simple evidence-based interventions can be challenging to scale-up and sustain. To ensure successful implementation it is essential to align with multilevel socioecological perspectives and assess the vast array of contextual factors that are at the core of better understanding successful implementation.
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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 | 0.040 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".