Bibliographic record
Abstract
This study explored Facebook™ to capture the prevalence of driving safety promotion user groups, obtain user demographic information, to understand if Facebook™ user groups influence reported driving behaviors, and to gather a sense of perceived effectiveness of Facebook™ for driving safety promotion targeted to young adults. In total, 96 driving safety Facebook™ groups (DSFGs) were identified with a total of 33,368 members, 168 administrators, 156 officers, 1,598 wall posts representing 12 countries. A total of 85 individuals participated in the survey. Demographic findings of this study suggest that driving safety promotion can be targeted to young and older adults. Respondents' ages ranged from 18 to 66 years. A total of 62% of respondents aged ≤ 24 years and 57.8% of respondents aged ≥ 25 years reported changing their driving-related behaviors as a result of reading information on the DSFGs to which they belonged. A higher proportion of respondents ≥ 25 years were significantly more likely to report Facebook™ and YouTube™ as an effective technology for driving safety promotion. This preliminary study indicates that DSFGs may be effective tools for driving safety promotion among young adults. More research is needed to understand the cognition of Facebook™ users as it relates to adopting safe driving behavior. The findings from this study present descriptive data to guide public health practitioners for future health promotion activities on Facebook™.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 0.071 |
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".