Integrating perceptions of the school neighbourhood environment with constructs from the theory of planned behaviour when predicting transport‐related cycling among Chinese college students
Bibliographic record
Abstract
Abstract Aim: Using a longitudinal design, the purpose of this study was to test a model integrating perceptions of the school neighbourhood environment with constructs from the Theory of Planned Behaviour (TPB) to predict transport‐related cycling among Chinese college students. Methods: A sample of 630 (M age = 18.95 years; SD = 1.16) Chinese college students completed baseline measures that included perceptions of the school neighbourhood environment, TPB constructs, transport‐related cycling, and covariates. Of those, 547 students (M age = 18.90 years; SD = 0.92) also completed measures of transport‐related cycling one month later. Results: Findings showed that intention had a direct effect on students’ transport‐related cycling, while attitudes, subjective norm, and perceived behavioural control predicted cycling indirectly via intention. Perceptions of the school neighbourhood environment did not provide an indirect effect on cycling via the TPB constructs, although some perceived environmental factors had indirect effects on intention via attitudes and subjective norm. Further, those who perceived better street connectivity showed a larger intention‐cycling relationship than those who perceived less street connectivity. Conclusions: Findings of this study highlight the importance of integrating perceptions of the school neighbourhood environment with the TPB constructs to explain transport‐related cycling through both intention formation and action control (i.e. translating intention into behaviour). The identified moderator of perceived street connectivity on the intention‐cycling relationship suggests that efficient cycling routes may impact action control of cycling. Future research applying dual process and action control models beyond TPB may contribute to the identification of the multilevel determinants of transport‐related cycling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".