Knowledge and behaviors of drunk-driving offenders in Guangzhou, China
Bibliographic record
Abstract
Jia, K., Fleiter, J., King, M., Sheehan, M., Ma, W., & Zhang, J. (2015). Knowledge and behaviors of drunk-driving offenders in Guangzhou, China. The International Journal Of Alcohol And Drug Research, 4(2), 151-158. doi:http://dx.doi.org/10.7895/ijadr.v4i2.203Aims: To better understand the knowledge and behaviors of drunk-driving offenders relating to alcohol use and driving in thecontext of recently amended Chinese legislation, and to investigate the involvement of alcohol-use disorders.Design: The study was a cross-sectional survey conducted in 2012.Setting and participants: Data were collected at a local jail and 101 participants were recruited while in detention.Measures: Questionnaire items examined demographic characteristics as well as practices and knowledge relating to alcohol useand driving. The Alcohol Use Disorders Identification Test (AUDIT) was used to assess hazardous drinking levels.Findings: Knowledge about the two legal limits for “drink driving” and for “drunk driving” was low, at 28.3% and 41.4%,respectively. AUDIT scores indicated that a substantial proportion of the offenders had high levels of alcohol-use disorders.Higher AUDIT scores were found among the least experienced drivers, those who lacked knowledge about the legal limits, andrecidivist drunk drivers.Conclusions: Limited awareness of legal alcohol limits might contribute to offending; high AUDIT scores suggest thathazardous drinking levels may also contribute. This study provides important information to assist in refining communityeducation and prevention efforts.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".