A profile of designated drivers and the people who use them: A survey of two provincial cities
Bibliographic record
Abstract
This paper reports the findings of a survey that was conducted in two provincial Queensland cities as part of an evaluation of a trial designated driver program. It provides a profile of designated drivers and those who use them; using data from the baseline questionnaire. The sample consisted of 405 individuals surveyed in 16 drinking venues in both cities (approximately 90% response rate). The participants were asked about their knowledge and use of designated driver, drinking behaviour, reported drink driving behaviour and demographic characteristics. There were 205 males (50.6%) and 200 females (49.4%) in the sample. The majority were aged under 30 years of age (63.5%). Around 15% of participants had been a designated driver in the last three months, with 70% having used a designated driver in the last three months. Almost14% of participants indicated that they were a designated driver on the night of interview, with almost one quarter of participants using a designated driver on the night of interview. Those who indicated that they were a designated driver on the night of the survey tended to be older, less likely to have reported drink driving, and consumed less alcohol in an average week than those who were not acting as designated drivers. Similar differences were found between those who had been a designated driver at least once before and those who had never been a designated driver. The results of the study have important implications for the design of designated driver programs and associated publicity campaigns. Limitations and other implications of the results are also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".