Predicting Transport‐Related Walking in Chinese Employees by Integrating Worksite Neighbourhood Walkability and Social Cognition
Bibliographic record
Abstract
BACKGROUND: As an accessible and inexpensive activity in daily life for employees, transport-related walking is a promising focus of physical activity initiatives. The purpose of this study was to integrate worksite neighbourhood walkability with the theory of planned behaviour (TPB) to predict transport-related walking in Chinese employees using a longitudinal design. METHODS: = 33.26 years; SD = 7.18) reported their social cognition and worksite neighbourhood environment perceptions at the baseline. Self-reported transport-related walking was measured at two time points, 1 month apart. RESULTS: Path analyses revealed that intention had a direct effect on walking, while attitudes, subjective norm, and perceived behavioural control had indirect effects on walking via intention. Past behaviour had a significant effect on walking, attenuating the intention-behaviour effect substantially. However, there was no indirect effect from perceived worksite neighbourhood walkability on walking through the TPB constructs. Furthermore, perceived neighbourhood walkability did not moderate the intention-walking relationship. CONCLUSIONS: Perceived worksite neighbourhood walkability had limited effects on transport-related walking, which seems to be a motivated and habitual behaviour. Habit-based interventions may be a priority over social cognitive and environmental change interventions, and future experimental studies are needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".