Perceived community environmental influences on eating behaviors: A Photovoice analysis
Bibliographic record
Abstract
People's perceptions of local food environments influence their abilities to eat healthily. PhotoVoice participants from four communities in Alberta, Canada took pictures of barriers and opportunities for healthy eating and shared their stories in one-on-one semi-structured interviews. Using a socioecological framework, emergent themes were organized by type and size of environment. Findings show that, while availability and access to food outlets influence healthy eating practices, these factors may be eclipsed by other non-physical environmental considerations, such as food regulations and socio-cultural preferences. This study identifies a set of meta-themes that summarize and illustrate the interrelationships between environmental attributes, people's perceptions, and eating behaviors: a) availability and accessibility are interrelated and only part of the healthy eating equation; b) local food is synonymous with healthy eating; c) local food places for healthy eating help define community identity; d) communal dining (commensality) does not necessarily mean healthy eating; e) rewarding an achievement or celebrating special occasions with highly processed foods is socially accepted; f) food costs seemed to be driving forces in food decisions; g) macro-environmental influences are latent in food decisions. Recognizing the interrelationship among multiple environmental factors may help efforts to design effective community-based interventions and address knowledge gaps on how sociocultural, economic, and political environments intersect with physical worlds.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".