MétaCan
Menu
Back to cohort
Record W2554145913 · doi:10.1080/14659891.2016.1232757

Alcohol’s harm to others in Switzerland in the year 2011/2012

2016· article· en· W2554145913 on OpenAlexaff
Simon Marmet, Gerhard Gmel

Bibliographic record

VenueJournal of Substance Use · 2016
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsHarmContext (archaeology)MedicinePopulationDemographyAlcohol consumptionHarassmentEnvironmental healthPsychologyAlcoholSocial psychologyGeography

Abstract

fetched live from OpenAlex

Background and Aims: Alcohol consumption not only causes harm to the drinker, but also affects other people around the drinker. The prevalence of being affected by others’ drinking was estimated in Switzerland for the year 2011/2012. Methods: Data were collected in the context of the Addiction Monitoring in Switzerland (AMIS). Two thousand four hundred and seventy four subjects participated in computer assisted telephone interviews in a representative survey of the Swiss adult (15+ years) population. Results: In the past 12 months, 52.2% of the Swiss population was affected by others’ drinking in some way. Young adults were affected more often than older persons (p < 0.001) and men were more often affected than women (OR = 0.84; p < 0.05). Compared to abstainers, low risk drinkers (OR = 1.46; p < 0.05), risky single occasion drinkers (RSOD) (OR = 1.95; p < 0.001), and heavy drinkers (OR = 1.88; p < 0.01) were more often affected. The dominant type of harm was harm in public space (like harassment or being afraid because of others’ drinking) which was reported by 45.7% of the sample. Conclusion: More than half of the Swiss population was affected by others’ drinking in at least one way. Alcohol use is associated with psychological and physiological burden for persons other than the drinkers themselves.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.258

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 head0.057
GPT teacher head0.305
Teacher spread0.248 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Substance UseSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207