Evaluation of a population health strategy to reduce distracted driving: Examining all “Es” of injury prevention
Bibliographic record
Abstract
BACKGROUND: Cell phone use while driving (CPWD) increases the risk of crashing and is a major contributor to injuries and deaths. The objective of this study was to describe the evaluation of a multifaceted, evidence-based population health strategy for the reduction of distracted driving. METHODS: A multipronged campaign was undertaken from 2014 to 2016 for 16- to 44-year-olds, based on epidemiology, focused on personal stories and consequences, using the "Es" of injury prevention (epidemiology, education, environment, enforcement, and evaluation). Education consisted of distracted driving videos, informational cards, a social media AdTube campaign, and a movie theater trailer, which were evaluated with a questionnaire regarding CPWD attitudes, opinions, and behaviors. Spatial analysis of data within a geographic information system was used to target advertisements. A random sample telephone survey evaluated public awareness of the campaign. Increased CPWD enforcement was undertaken by police services and evaluated by ARIMA time series modeling. RESULTS: The AdTube campaign had a view rate of >10% (41,101 views), slightly higher for females. The top performing age group was 18- to 24-year-olds (49%). Our survey found 61% of respondents used handheld CPWD (14% all of the time) with 80% reporting our movie trailer made them think twice about future CPWD. A stakeholder survey and spatial analysis targeted our advertisements in areas of close proximity to high schools, universities, near intersections with previous motor vehicle collisions, high traffic volumes, and population density. A telephone survey revealed that 41% of the respondents were aware of our campaign, 17% from our print and movie theater ads and 3% from social media. Police enforcement campaign blitzes resulted in 160 tickets for CPWD. Following campaign implementation, there was a statistically significant mean decrease of 462 distracted driving citations annually (p = 0.001). CONCLUSION: A multifaceted, evidence-based population health strategy using the Es of injury prevention with interdisciplinary collaboration is a comprehensive method to be used for the reduction of distracted driving. LEVEL OF EVIDENCE: Therapeutic, level IV.
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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 | 0.057 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".