Physical activity promotion in rural health care settings: A rapid realist review
Bibliographic record
Abstract
Physical activity promotion in health care settings is poorly understood and has limited uptake among health care providers. The environmental and health care context of rural communities is unique from urban areas and may interact to influence intervention delivery and success. The aim of this rapid realist review was to synthesize knowledge related to the promotion of physical activity in rural health and social care settings. We searched Medline EBSCO, CINAHL, PsychINFO, and SPORTDiscus for relevant publications. We included qualitative or quantitative studies reporting on an intervention to promote physical activity in rural health (e.g., primary or community care) or social (e.g., elder support services) care settings. Studies without a rural focus or well-defined physical activity/exercise component were excluded. Populations of interest included adults and children in the general population or clinical sub-population. Intervention mechanisms from included studies were mapped to the Behaviour Change Wheel (capability, opportunity, motivation (COM-B)). Twenty studies were included in our review. Most interventions focused on older adults or people with chronic disease risk factors. The most successful intervention strategies leading to increased physical activity behaviour included wearable activity trackers, and check-ins or reminders from trusted sources. Interventions with mechanisms categorized as physical opportunity, automatic motivation, and psychological capability were more likely to be successful than other factors of the COM-B model. Successful intervention activities included a method for tracking progress, providing counselling, and follow-up reminders to prompt behaviour change. Cultivation of necessary community partnerships and adaptations for implementation of interventions in rural communities were not clearly described and may support successful outcomes in future studies.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 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".