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Record W2733110652 · doi:10.1093/geroni/igx004.3729

ALCOHOL SELF-MEDICATION AMONG MIDDLE-AGED AND OLDER ADULTS

2017· article· en· W2733110652 on OpenAlexaff
Sarah L. Canham, Pia M. Mauro

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSelf-medicationAlcoholMedicinePresentation (obstetrics)PopulationGerontologyPsychiatryPsychologyClinical psychologyEnvironmental health

Abstract

fetched live from OpenAlex

Middle-aged and older adults use alcohol for various reasons, including to self-medicate. Self-medication is the use of alcohol or other substances to relieve discomforting physical/mental health symptoms or to cope with negative affect. Understanding the reasons why middle-aged and older adults use alcohol and providing alternatives to alcohol are important steps toward reducing the morbidity of alcohol use disorders in this population. The goals of this presentation are to review: the prevalence of self-medication with alcohol among adults in mid- and late-life; reasons for self-medication with alcohol; and potential outcomes of self-medication with alcohol in later life. We propose a conceptual model to help synthesize the literature and aid in hypothesis development and testing. We then review measurement issues, including how to identify middle-aged and older adults who may be self-medicating with alcohol, and discuss treatment and prevention opportunities. This presentation concludes by highlighting avenues for future research.

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)0.004
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.353
Teacher spread0.317 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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