Barriers and facilitators to physical activity in rural communities: using the Behaviour Change Wheel to identify intervention functions
Bibliographic record
Abstract
BACKGROUND: People living in rural communities face increased barriers and fewer opportunities for regular participation in physical activity compared to those living in urban centers. This project aimed to map barriers and facilitators of physical activity in rural communities to the Theoretical Domains Framework and Behaviour Change Wheel to identify intervention functions. METHODS: We conducted individual semi-structured interviews with participants from rural communities in two sparsely populated regions in British Columbia, Canada. Interview questions were framed using the Theoretical Domains Framework and focused on beliefs and lived experiences of physical activity. Deductive analysis involved identifying barriers and facilitators to physical activity and mapping them to the Theoretical Domains Framework and capability, opportunity, and motivation (COM-B) of the Behaviour Change Wheel. Barriers and facilitators were used to identify potential intervention functions and policy levers to change physical activity behaviour in rural communities. RESULTS: Participants included 46 individuals aged 22 to 77 years (36 women) living in rural communities (population size < 100-13,000 people). Barriers and facilitators to physical activity were predominant in the domains of Social Influences, Environmental Context and Resources, and Beliefs About Consequences. Specific barriers and facilitators were identified related to the availability of friends or family with whom to be physically active; weather-related factors; programming and infrastructure for physical activity; knowledge of local opportunities; and beliefs about healthy aging and fear of injuries. Based on mapping barriers and facilitators to the Behaviour Change Wheel, physical activity intervention strategies for rural communities should focus on environmental restructuring, modelling, and enablement. CONCLUSION: To improve physical activity in rural communities, interventions at municipal and provincial levels should focus on addressing barriers, such as infrastructure and policy supportive of physical activity, and leveraging existing facilitators, such as access to outdoor physical activity opportunities. CLINICAL TRIAL NUMBER: not applicable.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".