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Cost of privatisation versus government alcohol retailing systems: Canadian example

2011· article· en· W1809060032 on OpenAlexafffundabout
Svetlana Popova, Jayadeep Patra, Anna Sarnocinska-Hart, William Gnam, Norman Giesbrecht, Jürgen Rehm

Bibliographic record

VenueDrug and Alcohol Review · 2011
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsPublic Health OntarioUniversity of TorontoInstitute for Work & HealthCentre for Addiction and Mental Health
FundersHealth CanadaCentre for Addiction and Mental Health
KeywordsGovernment (linguistics)BusinessAlcoholChemistry

Abstract

fetched live from OpenAlex

INTRODUCTION AND AIMS: Alcohol retail monopolies have been established in many countries to restrict alcohol availability and thus, minimise alcohol-related harm.The aim of this study was to estimate the impact of the privatisation of alcohol sales on the burden and direct health-care, law enforcement costs and indirect costs (lost productivity due to disability or premature mortality) in Canada. DESIGN AND METHODS: Simulation modelling. International Guidelines for the Estimation of the Avoidable Costs of Substance Abuse were used. All burden and costs were compared with the baseline taken from the aggregate Cost Study on Substance Abuse in Canada 2002. RESULTS: If all Canadian provinces and territories were to privatise alcohol sales we assume that consumption would increase from 10% to 20% based on available Canadian literature. Under the 10% scenario the costs would increase from 6% ($828 million) and under the 20% scenario costs would increase 12% ($1.6 billion).This increase is substantially greater than the tax and mark-up revenue gained from increased sales,and represents a net loss. DISCUSSION AND CONCLUSIONS: Alcohol-attributable burden and associated costs will increase markedly if all Canadian provinces and territories gave up the government alcohol retailing systems.For public health and economic reasons, governments should continue to have a strong role in alcohol retailing.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.998

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)0.0000.000

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 head0.185
GPT teacher head0.322
Teacher spread0.137 · 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

Citations12
Published2011
Admission routes3
Has abstractyes

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