Community health promotion programs for older adults: What helps and hinders implementation
Bibliographic record
Abstract
BACKGROUND AND AIMS: Despite the many known health benefits of physical activity (PA), older adults are the least active citizens in many countries. Regular PA significantly decreases the odds of functional limitation and social disengagement. However, there is a dearth of publicly funded support services for older adults. The primary objective of this study is to conduct a formative evaluation to examine the implementation of community-driven health promotion programs for older adults in British Columbia, Canada. METHODS: The Active Aging Grant (AAG) initiative funded 30 community-based organizations in British Columbia to design and deliver community-driven health promotion programs for older adults, with an explicit focus on PA and social connectedness. Guided by the Framework for Successful Implementation, we recruited program coordinators and participants and used semistructured interview guides to focus on design, delivery, and experience within the program. Framework analysis was used with NVivo 11. RESULTS: Thirty-six in-depth, semistructured interviews were conducted in 2017, after program completion. Data saturation was achieved after interviewing 10 coordinators and 26 program participants from seven of the organizations. Eighteen were female; nine were male; 68% fell in the age range of 65-84. We detail the innovation characteristics, provider characteristics, and contextual factors that facilitate and impede program implementation. Aspects that facilitate implementation include that they promote PA, foster social connectedness, and address isolation and loneliness; personal accountability; affordability; program design; providers' appropriate skills; community collaborations; and transportation support. Aspects that hinder implementation include lack of resources for marketing and communications, lack of volunteers and dedicated staff, and access to transportation. We also highlight two themes that emerged outside the theoretical framework, the roles of gender and funding in program implementation. CONCLUSIONS: As part of a formative evaluation, the information will help adapt and enhance implementation of a larger scale-out intervention aimed to increase PA and social connectedness amongst older adults in British Columbia, Canada.
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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 | 0.093 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".