How perceptions of community environment influence health behaviours: using the Analysis Grid for Environments Linkedto Obesity Framework as a mechanism for exploration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Overweight and obesity are influenced by a complex interplay of individual and environmental factors that affect physical activity and healthy eating. Nevertheless, little has been reported on people's perceptions of those factors. Addressing this critical gap and community partner needs, this study explored how people perceived the influence of micro- and macroenvironmental factors on physical activity and healthy eating. METHODS: Community partners wanted the study results in a format that would be readily and easily used by local decision makers. We used photovoice to engage 35 community members across four municipalities in Alberta, Canada, and to share their narratives about their physical activity and healthy eating. A combination of inductive and deductive analysis categorized data by environmental level (micro vs. macro) and type (physical, political, economic, and sociocultural), guided by the Analysis Grid for Environments Linked to Obesity Framework. RESULTS: Participants conceptualized health-influencing factors more broadly than physical activity and healthy eating to include "community social health." Participants spoke most often about the influence of the microenvironment (n = 792 ANGELO Framework coding tallies) on their physical activity, healthy eating and community social health in comparison to the macroenvironment (n = 93). Photovoice results provided a visual narrative to community partners and decision makers about how people's ability to make healthy choices can be limited by macroenvironmental forces beyond their control. CONCLUSION: Focussing future research on macro- and microenvironmental influences and localized community social health can inform practice by providing strategies on how to implement healthy changes within communities, while ensuring that research and interventions echo diverse people's perceptions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it