MétaCan
Menu
Back to cohort
Record W2062056267 · doi:10.1080/09687630310001597087

Serving alcohol at home: what do most people do? Findings from a 2001 ontario adult survey

2004· article· en· W2062056267 on OpenAlexaffabout
Lise Anglin, Norman Giesbrecht, Anca Ialomiteanu, Larry F. Grand, Robert B. Mann, Janet McAllister

Bibliographic record

VenueDrugs Education Prevention and Policy · 2004
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsDrunk drivingSample (material)Service (business)Environmental healthAlcoholPsychologyHuman factors and ergonomicsMedicineBusinessPoison controlMarketing

Abstract

fetched live from OpenAlex

In Ontario, some court cases have involved attempts to sue social hosts for damage caused by the behaviour of drunken guests. Such legal actions give rise to the question of risks and responsibilities accruing to social hosts who serve alcohol. Using a sample of 1395 male and female adult residents of Ontario, the authors present self-report survey data concerning frequency of serving alcohol to guests, methods of handling guests who have too much to drink, prevention of drunk driving, service of alcohol to underage persons, and offering food and non-alcoholic drinks when serving alcohol at home. The results show high levels of intended safe practices overall, along with some areas for concern. Notably, about one-third of the total sample had had guests judged to be too drunk to drive home safely. A multivariate analysis confirmed significant differences associated with sex and age. The authors recommend the creation and evaluation of programmes to upgrade home-hosting skills as an adjunct to systemic alcohol control policies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.997

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.037
GPT teacher head0.410
Teacher spread0.373 · 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.

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

Citations1
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueDrugs Education Prevention and PolicySame topicHomelessness and Social IssuesFrench-language works237,207