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RESPONSE TO COMMENTARIES

2012· letter· en· W2334110094 on OpenAlexaboutno aff
Flávio Pechansky, Aruna Chandran

Bibliographic record

VenueAddiction · 2012
Typeletter
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersWorld Health Organization
KeywordsLatin AmericansPsychological interventionEnforcementDeterrence theoryPoison controlSuicide preventionLaw enforcementPerceptionPublic healthOccupational safety and healthHuman factors and ergonomicsPsychologyInjury preventionCriminologySocial psychologyMedicineEnvironmental healthPolitical scienceLawPsychiatry

Abstract

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We appreciate the insightful responses provided by Drs Pinsky 1, Mann 2, Obot 3 and Caetano 4 on our commentary 5 regarding differences in southern versus northern hemisphere approaches to the control and prevention of drinking and driving (DWI). Dr Obot's point about the significant burden of alcohol-associated road traffic mortality in sub-Saharan Africa is absolutely correct; the World Health Organization (WHO)'s Global Status Report on Road Safety highlights DWI as a major problem in many countries in Africa, Asia and Latin America 6. Evidence-based interventions are needed urgently across most countries in order to combat this important issue. We also appreciate Dr Mann's points about the continued work that needs to be conducted in the United States and Canada; issues of further lowering the legal blood alcohol concentration (BAC) limit and conducting random breath testing (RBT) are also very much on our minds, as is the constant tension between rigorous public health evidence versus the well-organized lobby of the alcohol industry. In discussing the behavior paradox between US-born Hispanics versus Hispanic immigrants, Dr Caetano highlights an important point—the perception of deterrence may, in fact, be one of the factors that defines behaviors such as DWI, and we might hypothesize that immigrants from countries with lax attitudes towards DWI would have a strong perception of the law and its enforcement when driving in a more organized environment. Gibbs states that deterrence can be ‘[ . . . ] thought of as the omission of an act as a response to the perceived risk and fear of punishment for contrary behavior’7. As Snortum mentions 8, it is the threat of formal sanctions that can be weakened or increased according to drivers' perceptions of how laws are enforced; this is why RBT might have a role in reducing the prevalence of DWI 9, 10. A combination of efforts stemming from public health professionals, government and law-enforcement personnel, and advocacy organizations such as Mothers against Drunk Driving (MADD), could play a strong role in the efforts to decrease DWI: the goals would be legislative lobbying and increased public awareness, in conjunction with strong and targeted enforcement. The role of these organizations has been recognized in providing critical services in advocacy, legislative lobbying, public education and victim support in the United States; the National Highway Traffic Safety Administration, among others, recognizes the potential impact of these organizations and frequently partners them in control efforts 11-13. There are many similar advocacy groups in the northern and southern hemispheres 14, 15 with track records of lobbying and community education; such organizations can become even more effective in advocacy and public education through involvement in government-run interventions, and resource and training support. We hope the ‘Não foi um acidente’[‘It was not an accident’] movement 16 mentioned by Dr Pinsky will catch on in Brazil and many other countries; recognizing that drinking and driving is a preventable offense instead of a ‘random accident’ is a change in thinking and culture that, if achieved, will go a long way towards combating this significant public health concern. None. FP was funded by the National Secretariat for Drug and Alcohol Policies, Brazil, the Bloomberg Foundation, RS-10 Road Safety in 10 Countries Project and the Fogarty International Center. AC was funded by the Bloomberg Foundation, RS-10 Road Safety in 10 Countries Project and GAVI's Hib Initiative, Johns Hopkins University, Department of Pediatrics.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.281
Teacher spread0.253 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2012
Admission routes1
Has abstractyes

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