Drink driving among Indigenous people in far north Queensland and Northern New South Wales: a summary of the qualitative findings
Bibliographic record
Abstract
In response to the threat that drink drivers pose to themselves and others, drink driving programs form an important part of a suite of countermeasures used in Australia and internationally. Unlike New Zealand/Aotearoa, United States and Canada that have programs catering for their First Peoples, all Australian programs are designed for the general driver population. The aim of this study was to identify the factors that contribute to Indigenous drink driving in order to inform appropriate recommendations related to developing a community-based program for Indigenous communities. Broader drivers licensing policy recommendations are also discussed. A sample of 73 Indigenous people from Queensland and in New South Wales with one or more drink driving convictions completed a semi-structured interview regarding their drink driving behaviour. Participants were asked to disclose information regarding their drink driving history, and alcohol and drug use. If participants self-reported no longer drink driving, they were probed about what factors had assisted them to avoid further offending. Key themes which emerged to maintain drink driving include motivations to drink and drive, and belief in the ability to manage the associated risks. Factors that appeared to support others from avoiding further offending include re-connecting with culture and family support. A range of recommendations regarding delivery and content of a program for regional and remote communities as well as other policy implications are 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".