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Record W2563426419 · doi:10.1093/alcalc/agw090

The Alcohol Use Disorders Identification Test (AUDIT): Exploring the Factor Structure and Cutoff Thresholds in a Representative Post-Conflict Population in Northern Uganda

2016· article· en· W2563426419 on OpenAlex
Alden Blair, Margo Pearce, Achilles Katamba, Samuel S. Malamba, Herbert Muyinda, Martin T. Schechter, Patricia M. Spittal

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAlcohol and Alcoholism · 2016
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAlcohol Use Disorders Identification TestAuditPopulationPsychologyPsychological interventionDemographyPer capitaAlcohol use disorderPoison controlEnvironmental healthInjury preventionMedicinePsychiatryAlcoholSociologyAccounting

Abstract

fetched live from OpenAlex

AIMS: Despite increased use of the Alcohol Use Disorders Identification Test (AUDIT) in sub-Saharan Africa, few studies have assessed its underlying conceptual framework, and none have done so in post-conflict settings. Further, significant inconsistencies exist between definitions used for problematic consumption. Such is the case in Uganda, facing one of the highest per-capita alcohol consumption levels regionally, which is thought to be hindering rebuilding in the North after two decades of civil war. This study explores the impact of varying designation cutoff thresholds in the AUDIT as well as its conceptual factor structure in a representative sample of the population. METHODS: In all, 1720 Cango Lyec Project participants completed socio-economic and mental health questionnaires, provided blood samples and took the AUDIT. Participant characteristics and consumption designations were compared at AUDIT summary score thresholds of ≥3, ≥5 and ≥8. Confirmatory factor analyses (CFA) explored one-, two- and three-factor level models overall and by sex with relative and absolute fit indicators. RESULTS: There were no significant differences in participant demographic characteristics between thresholds. At higher cutoffs, the test increased in specificity to identify those with hazardous drinking, disordered drinking and suffering from alcohol-related harms. All conceptual models indicated good fit, with three-factor models superior overall and within both sexes. CONCLUSION: In Northern Uganda, a three-factor AUDIT model best explores alcohol use in the population and is appropriate for use in both sexes. Lower cutoff thresholds are recommended to identify those with potentially disordered drinking to best plan effective interventions and treatments. SHORT SUMMARY: A CFA of the AUDIT showed good fit for one-, two, and three-factor models overall and by sex in a representative sample in post-conflict Northern Uganda. A three-plus total AUDIT cutoff score is suggested to screen for hazardous drinking in this or similar populations.

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.

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 score0.024
Threshold uncertainty score0.999

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 communication0.0000.001
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 head0.050
GPT teacher head0.303
Teacher spread0.253 · 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