Community Lenses Revealing the Role of Sociocultural Environment on Physical Activity
Bibliographic record
Abstract
PURPOSE: To identify perceptions of how sociocultural environment enabled and hindered physical activity (PA) participation. DESIGN: Community-based participatory research. SETTING: Two semirural and two urban communities located in Alberta, Canada. PARTICIPANTS: Thirty-five people (74.3% females, 71.4% aged 25-64 years) across the four communities. METHOD: PhotoVoice activities occurred over 3 months during the spring of 2009. Participants were asked to document perceived environmental attributes that might foster or inhibit PA in their community. Photographs and narratives were shared in one-on-one interviews. Line-by-line coding of the transcripts was independently conducted by two researchers using an inductive approach. Codes were arranged into themes and subthemes, which were then organized into the Analysis Grid for Environments Linked to Obesity (ANGELO) framework. RESULTS: Six main themes (accompanied by subthemes) emerged: sociocultural aesthetics, safety, social involvement, PA motivation, cultural ideas of recreation, and car culture. Representative quotes and photographs illustrate enablers and obstacles identified by participants. CONCLUSION: This PhotoVoice study revealed how aspects of participants' sociocultural environments shaped their decisions to be physically active. Providing more PA resources is only one step in the promotion of supportive environments. Strategies should also account for the beautification and maintenance of communities, increasing feelings of safety, enhancement of social support among community members, popularization of PA, and mitigating car culture, among others.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".