The impact of policies regulating alcohol trading hours and days on specific alcohol-related harms: a systematic review
Bibliographic record
Abstract
BACKGROUND: of alcohol sales associate with changes in alcohol-related harm in both directions. However, to the best of our knowledge, no comprehensive systematic reviews had examined the effect of policies restricting time of alcohol trading on specific alcohol-related harms. OBJECTIVE: To compile existing evidence related to the impact of policies regulating alcohol trading hours/days of on specific harm outcomes such as: assault/violence, motor vehicle crashes/fatalities, injury, visits to the emergency department/hospital, murder/homicides and crime. METHODS: Systematic review of literature studying the impact of policies regulation alcohol trading times in alcohol-related harm, published between January 2000 and October 2016 in English language. RESULTS: Results support the premise that policies regulating times of alcohol trading and consumption can contribute to reduce injuries, alcohol-related hospitalisations/emergency department visits, homicides and crime. Although the impact of alcohol trading policies in assault/violence and motor vehicle crashes/fatalities is also positive, these associations seem to be more complex and require further study. CONCLUSION: Evidence suggests a potential direct effect of policies that regulate alcohol trading times in the prevention of injuries, alcohol-related hospitalisations, homicides and crime. The impact of these alcohol trading policies in assault/violence and motor vehicle crashes/fatalities is less compelling.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".