Pedestrian Road-Crossing Behaviours: A Protocol for an Explanatory Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Pedestrian crossing is an important traffic safety concern. The aim of this paper is to report the protocol for a sequential explanatory mixed methods study that set out to determine the pedestrians' traffic behaviors, the associated factors and exploring the perception of young people about the traffic risky behaviors in crossing the road. The ultimate purpose of the study is to design a preventive and cultural based strategy to promote young people's health. METHODS: This is a sequential explanatory mixed methods design. The study has two sequential phases. During the first phase, a population-based cross-sectional survey of a sample of young people will be conducted using the proportional random multistage cluster sampling method, in Tehran, Iran. Data will be collected by a questionnaire including items on socio-demographic information, items on measuring social conformity tendency, and questions on subjective norms, attitudes, and perceived behavioral control based on the Theory of Planned behavior. In the second phase, a qualitative study will be conducted. A purposeful sampling strategy will be used and participants who can help to explain the quantitative findings will be selected. Data collection in qualitative phase will be predominately by individual in-depth interviews. A qualitative content analysis approach will be undertaken to develop a detailed understanding of the traffic risky behaviors among young pedestrians. CONCLUSION: The findings of this explanatory mixed methods study will provide information on traffic risky behaviors in young pedestrians. The findings will be implemented to design a cultural based strategy and intervention programs.
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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 | 0.082 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.010 |
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".