A comparative study of personal health behavior in professional and amateur drivers under COVID-19 conditions
Bibliographic record
Abstract
Background — The ongoing COVID-19 pandemic has significantly impacted various industries, including transportation. Taxi drivers and amateur drivers have been on the frontline providing essential services while facing an increased risk of contracting the virus. Objective — This study aims to examine and compare the personal health behaviors adopted by taxi drivers vs. private vehicle drivers in the fight against COVID-19. Understanding these behaviors can help develop strategies to protect and support the health and safety of drivers and passengers. Methods — This cross-sectional study was conducted in 2022 in Tabriz, East Azerbaijan Province, Iran. A total of 700 drivers participated in the study, including 343 (49%) taxi drivers and 357 (51%) amateur drivers. The research team developed a questionnaire on personal health behaviors regarding COVID-19. The validity and reliability of the instrument were assessed. The personal health behaviors of taxi drivers vs. private vehicle drivers regarding COVID-19 were then examined. Results — Our findings showed that the percentage of health-promoting behaviors (such as wearing masks in public places, disposing of masks in a trash can with a lid, using masks correctly, washing hands with soap and water, and using alcohol-based hand sanitizers) were significantly higher among private vehicle drivers than among taxi drivers (p<0.001). Conclusion — The results show that private vehicle drivers adhere stricter to health guidelines than taxi drivers. Improving taxi drivers’ awareness and responsibility can help prevent COVID-19. Targeted interventions by policymakers and transportation companies can improve the safety and health of taxi drivers and their passengers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".