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Record W2726961080 · doi:10.1080/15332640.2017.1326863

The onset and progression of alcohol use disorders: A qualitative study from Goa, India

2017· article· en· W2726961080 on OpenAlexaff
Nathalie MacKinnon, Urvita Bhatia, Abhijit Nadkarni

Bibliographic record

VenueJournal of Ethnicity in Substance Abuse · 2017
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsMcGill University
FundersWellcome Trust
KeywordsThematic analysisMedicineQualitative researchAlcohol use disorderExplanatory modelBoredomPeer pressurePopulationEnvironmental healthPublic healthClinical psychologyPsychologyPsychiatryAlcoholSocial psychologyNursing

Abstract

fetched live from OpenAlex

Quantitative evidence about the burden of alcohol use disorders (AUDs) needs to be complemented with a nuanced qualitative understanding of explanatory models to help supplement public health strategies that are too often steeped uncritically in biomedical models. The aim of this study was to identify the role of various factors in the onset and persistence of AUD and recovery from AUD. This was a qualitative study nested in a population cohort from Goa, India. In-depth interviews of men with incident, recovered, and persistent AUD covered topics such as changes in drinking habits over time, perceptions and experiences about starting/stopping drinking, and so on. Data were analyzed using thematic analysis. Reasons to begin drinking included social drinking, functional use of alcohol, stress, and boredom. Progression to problematic drinking patterns was characterized by drinking alone, alternating between abstinent and heavy drinking periods, and drinking based on the availability of finances. Some enablers to reduce/stop drinking included consequences of drinking lifestyle and personal resolve; some barriers included availability of alcohol at social events and stress. Some reasons for persisting heavy use of alcohol included lack of family support, physical withdrawal symptoms, peer pressure, stress, and easy availability. This article offers a strong conceptualization and nuanced understanding of AUD across a spectrum of developmental courses. This adds to the limited literature on explanatory models of AUD in India and identifies potential targets for prevention and treatment strategies for AUD in low- and middle-income country settings.

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)0.001
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 score0.134
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.075
GPT teacher head0.418
Teacher spread0.343 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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