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Record W2138755883 · doi:10.1080/14659890701262189

Alcohol marketing and retailing: Public opinion and recent policy developments in Canada

2007· article· en· W2138755883 on OpenAlexaffabout
Norman Giesbrecht, Anca Ialomiteanu, Lise Anglin, Edward M. Adlaf

Bibliographic record

VenueJournal of Substance Use · 2007
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsContext (archaeology)Logistic regressionPublic opinionPopulationPublic policyOrdered logitConsumption (sociology)Alcohol consumptionVariablesPsychologyDemographic economicsMarketingBusinessEnvironmental healthPolitical scienceAlcoholEconomicsGeographyMedicineEconomic growthPoliticsSociologySocial science

Abstract

fetched live from OpenAlex

Recent developments in alcohol policy in Canada, particularly those pertaining to alcohol marketing and retailing, provide the context for this study of public opinion on alcohol policy topics. Three national surveys were conducted in 1989, 1994 and 2004. Respondents to the telephone interviews were sampled by province to be representative of the population aged 15 and older; numbers ranged from 4658 (2004) to 12,155 (1994). The key variables were gender, age group, province, education, drinking pattern and frequency of heavy drinking. Ten questions about alcohol policy were included in the analysis, but not all questions were asked in all 3 years. The logistic regression models contained a year effect, independent variable effect, and a year‐by‐independent variable effect. There was considerable variation in support across policy topics, the rank order being similar from year to year. There was somewhat less support for those items, e.g. higher taxes or fewer outlets, considered to be effective by evaluation studies. A major finding was a decline in support over time. Women, older respondents, and lighter drinkers and abstainers were more likely to be supportive of alcohol control policies. The analyses also revealed interaction between provinces and rate of change in declining support. The authors hypothesize that intensive marketing and retailing of alcohol may be important factors in declining public support. Declining support for alcohol policy is expected to have implications for controlling damage and costs related to increasing alcohol consumption.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.066
GPT teacher head0.300
Teacher spread0.234 · 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

Citations29
Published2007
Admission routes2
Has abstractyes

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