Do alcohol pricing and availability policies have differential effects on sub-populations? A commentary
Bibliographic record
Abstract
Giesbrecht, N., Wettlaufer, A., Cukier, S., Geddie, G., Gonçalves, A., & Reisdorfer, E. (2016). Do alcohol pricing and availability policies have differential effects on sub-populations? A commentary. The International Journal Of Alcohol And Drug Research, 5(3), 89-99. doi:http://dx.doi.org/10.7895/ijadr.v5i3.227Aims: Numerous policies have been shown to reduce the harm from alcohol; however, not all sub-populations respond similarly to policy interventions. This paper explores the specific effects of alcohol pricing policies and controls regarding physical availability on different types of harms from alcohol as well as on different sectors of the population, including impacts by gender, age, and drinking patterns.Design, Setting, Participants, and Measures: We focus on two dimensions. The first is alcohol pricing and taxation; the second is alcohol availability, comprising type of alcohol control system, outlet density, and hours/days of sale. We focused on peer-reviewed research and reviews published from 2005–2015, using several databases: PsycINFO, MEDLINE/PubMed, and Cochrane.Findings: Precautionary alcohol prices have substantial harm reduction potential, particularly among youth and high-risk drinkers. Restrictions on outlet densities and hours/days of sale impact the drinking patterns of underage youth, reduce high-risk drinking, and reduce alcohol-related harm. A reduction in prices or an increase in alcohol availability are associated with increase in high-risk drinking or alcohol-related harm.Conclusions: Future work should examine these policy measures in light of socioeconomic status and cultural factors, as well as impacts of policy interventions on evidence of harm to others from alcohol.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".