Driver’s Knowledge About the Use of Drug and Traffic Accident in Riau Indonesia
Bibliographic record
Abstract
Our study aimed to investigate the influence of socio-demographic, knowledge, attitude, toward the change in driving behavior. This research was conducted with cross-sectional study design, during the period of December 2016 until April 2017. The research instrument used was a questionnaire from Driving Under the Influence of Drugs, Alcohol, and Medicines (DRUID) project with modification. The descriptive statistics and logistic regression analysis was used. Our research revealed that from 100 respondents, about 10% male was available to change to reported behavior in frequency driving than female. About 11% of respondents aged 35–67 years old decided to change in frequency driving. Approximately 14% of respondents with higher education level were changing in reported behavior of frequency driving. Reported behavior in frequency driving was influenced by information received from health care providers and attitude about the consequences of driving under the influence of impairing medicines factors (p-value 0.006 and 0.028). Changing reported behavior in frequency driving can be predicted by information received from health care providers and attitudes. In the future, we need to build effective communication and ensuring patients receive information about driving-impairing medicines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".