Integrating the Perceived Neighborhood Environment and the Theory of Planned Behavior When Predicting Walking in a Canadian Adult Sample
Bibliographic record
Abstract
PURPOSE: To integrate the characteristics of the perceived environment with the theory of planned behavior (TPB) to determine (1) whether the TPB mediates relations among environmental characteristics and walking, and (2) whether the environment moderates TPB-walking relations. DESIGN: Cross-sectional. SETTING: South Vancouver Island, British Columbia, Canada. SUBJECTS: Random sample of 351 adults (36% response rate). MEASURES: Participants completed measures of the perceived neighborhood environment, the TPB, and walking behavior that was assessed using an adapted Godin leisure time questionnaire. RESULTS: Results using structural equation modeling indicated that the TPB mediated the environment-walking relationship. Specifically, retail land-mix use and neighborhood aesthetics were associated with walking through affective and instrumental attitudes. Results using moderated regression analyses showed that recreation land-mix use moderated the intention-behavior relationship, with those individuals who perceived closer access to recreation facilities having a larger intention-behavior relationship. A significant moderating effect for crime on the instrumental attitude-intention relationship was also identified, but the effect size was small to trivial. CONCLUSIONS: These results suggest that the perceived neighborhood may influence walking through attitudes and may also influence the intention-behavior gap. Prospective studies using objective walking and environment data are required to improve the veracity of the findings and to identify possible causal relationships.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".