Does communication support the promotion of cycling for transportation? Results from an experiment to test messaging strategies
Bibliographic record
Abstract
Introduction Active transportation can contribute to increase levels of PA , but to date interventions seem to have limited effects. Since communication approaches might contribute to intervention effectiveness, the aim of this study was to examine the effect of diverse messaging strategies aimed at promoting cycling for transportation. Methods A 2 × 2 × 2 factorial design was adopted. The experiment was conducted among 313 adults in the province of Québec, Canada. To be eligible, participants had to be aged between 18 and 54 years and currently employed. The main and interaction effects of different messaging strategies on information processing outcomes (perceived argument strength and involvement) and on the three-week follow-up intention and behavior were examined. Variables were assessed by means of questionnaires. Analyses controlled for baseline attitude toward cycling for transportation. Results Adjusted ANCOVAs revealed a main effect of the self-efficacy ( p = 0.03) condition on involvement. A significant main effect of attitude ( p = 0.008) and self-efficacy ( p = 0.007) messaging strategies was observed on perceived argument strength. No other main or significant interaction effect was observed for these information processing outcomes. The GEE models revealed a significant time X implementation intentions interaction effect on intention ( p = 0.02). No significant main or interaction effect was observed on cycling at follow-up. Conclusions No clear pattern of effect was observed for the tested messages, but results from this study helped increase our knowledge concerning the effects of specific message content. Results also suggest that integrating messages pertaining to attitude, self-efficacy, and implementation intentions could support (albeit modestly) public health interventions aimed at promoting cycling for transportation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".