Factors Influencing Pedestrian Traffic Collision in Iran: A Qualitative Content Analysis.
Bibliographic record
Abstract
Objectives: This study aimed to explore experts' perspectives on the factors influencing pedestrian traffic collisions in Iran. Methods: This qualitative study was conducted using conventional content analysis with an inductive approach from September 2023 to March 2024. Twenty-six experts were purposefully selected from across Iran. Data were collected through individual face-to-face interviews, guided by a semi-structured interview, developed by a panel of experts and contained open-ended questions. Data analysis was performed manually using the Graneheim and Lundman approach (2004). To ensure trustworthiness, four strategies proposed by Lincoln and Guba were employed. Results: The results revealed two main categories: direct factors and underlying factors, comprising nine subcategories. Direct factors included five subcategories: driver, pedestrian, roads and streets, vehicle, and geographic factors. Underlying factors included four subcategories: governance factors, social determinants, cultural conditions, and economic status. Conclusion: The study identified key risk factors associated with pedestrian collisions according to experts' experiences. We recommend further qualitative studies to explore high-risk behaviors among pedestrians and drivers in depth. Additionally, systematic reviews should examine strategies employed by developing and successful countries to prevent or reduce pedestrian collisions.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".