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Record W2197101219

Drink driving among Indigenous people in far north Queensland and Northern New South Wales: a summary of the qualitative findings

2015· article· en· W2197101219 on OpenAlexaboutno aff
Michelle S. Fitts, Gavan R. Palk

Bibliographic record

VenueQUT ePrints (Queensland University of Technology) · 2015
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsAotearoaIndigenousProject commissioningPublishingSuicide preventionHuman factors and ergonomicsPoison controlPopulationInjury preventionQualitative researchPublic relationsPsychologyEnvironmental healthMedicinePolitical scienceSociologySocial scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.242
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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