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Record W2258584151 · doi:10.1177/0898264315589579

Association of Alcohol Use and Loneliness Frequency Among Middle-Aged and Older Adult Drinkers

2015· article· en· W2258584151 on OpenAlexaff
Sarah L. Canham, Pia M. Mauro, Christopher N. Kaufmann, Andrew Sixsmith

Bibliographic record

VenueJournal of Aging and Health · 2015
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsSimon Fraser University
FundersNational Institute on Drug AbuseNational Institute on Aging
KeywordsLonelinessBinge drinkingOddsMedicineAlcoholAlcohol consumptionPoison controlAssociation (psychology)Multinomial logistic regressionDemographyInjury preventionSuicide preventionOdds ratioLogistic regressionEnvironmental healthGerontologyPsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: We examined the association between alcohol use, at-risk drinking, and binge drinking, and loneliness in a sample of middle-aged and older adults. METHOD: We studied participants aged 50+ years from the 2008 wave of the Health and Retirement Study who reported alcohol use. We ran separate multinomial logistic regressions to assess the association of three alcohol use outcomes (i.e., weekly alcohol consumption, at-risk drinking, and binge drinking) and loneliness. RESULTS: After adjusting for covariates, being lonely was associated with reduced odds of weekly alcohol consumption 4 to 7 days per week, but not 1 to 3 days per week, compared with average alcohol consumption 0 days per week in the last 3 months. No association was found between at-risk drinking or binge drinking and loneliness. DISCUSSION: Results suggest that among a sample of community-based adults aged 50+, loneliness was associated with reduced alcohol use frequency, but not with at-risk or binge drinking.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.085
GPT teacher head0.333
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 source (direct Gemma or distilled Codex), 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

Citations100
Published2015
Admission routes1
Has abstractyes

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