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Record W4404661016 · doi:10.1093/pubmed/fdae288

An evaluation of the impact of a national Minimum Unit Price on alcohol policy on alcohol behaviours

2024· article· en· W4404661016 on OpenAlexaff
Gretta Mohan

Bibliographic record

VenueJournal of Public Health · 2024
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsTrinity College
FundersEconomic and Social Research Council
KeywordsAlcohol Use Disorders Identification TestEnvironmental healthAuditMedicineSocioeconomic statusPopulationPoison controlDemographyAttritionPublic healthUnit (ring theory)Injury preventionPsychologyBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: In 2018, Scotland pioneered national legislation which set a Minimum Unit Price (MUP) of 50 pence (∼US$0.64, €0.59) per unit of UK alcohol sold (8 g/10 ml). To inform policy development, we examine the policy effect using the Alcohol Use Disorders Identification Test (AUDIT-C), employing longitudinal data for over 17 200 individuals. METHODS: The effect of MUP on AUDIT-C scores is inferred by employing difference-in-difference regression. Pre- and post-intervention alcohol behaviours of individuals from Scotland are compared to a matched 'control' from England. Drinking at hazardous and harmful levels could be identified, as well as the frequency of alcohol consumption, number of drinks and heavy episodic drinking. Estimates adjust for demographic, socioeconomic and health characteristics. Potential inequalities by gender, age and household income are examined. RESULTS: MUP led to an estimated 5.3% reduction in the number of drinks consumed on drinking occasions, though a statistically significant effect on overall reported AUDIT-C scores or drinking at hazardous levels was not detected, with few differential effects for subgroups. CONCLUSIONS: Differences in the findings of this research compared to other studies may be explained by differences in population coverage collected in the survey data, compared to more comprehensive, population-wide administrative data, as well as sample attrition.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.159
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.244
GPT teacher head0.499
Teacher spread0.255 · 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
Published2024
Admission routes1
Has abstractyes

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