Functional-Belief-Based Alcohol Use Questionnaire (FBAQ) as a Pre-Screening Tool for High-Risk Drinking Behaviors among Young Adults: A Northern Thai Cross-Sectional Survey Analysis
Bibliographic record
Abstract
BACKGROUND: an alcohol-use disorders identification test (AUDIT) is a standard screening tool for high-risk drinking behavior. Standard drink calculation is difficult to comprehend and may lead to inaccurate estimates. This study intended to develop a practical pre-screening tool for the identification of high-risk drinkers among young adults. METHODS: a cross-sectional survey was conducted in Northern Thailand from July 2016 to December 2016. Data was collected on relevant characteristics and health beliefs about drinking. The 12-month AUDIT was used as the reference standard. Logistic regression was used for the score derivation. The discriminative ability was measured with an area under the receiver operating characteristic curve (AuROC). RESULT: a total of 1401 young adults were included. Of these, 791 people (56.5%) were current drinkers. Three functional-belief items were identified as independent predictors of high-risk drinking and were used to develop the functional-belief-based alcohol-use questionnaire (FBAQ). The FBAQ demonstrated an acceptable discriminative ability-AuROC 0.74 (95% confidence interval (CI) 0.70, 0.77). CONCLUSION: The FBAQ contains only three simple belief questions and does not require unintelligible standard drink calculation. Implementing the FBAQ score and the AUDIT in a serial manner might be a more effective method in a mass-screening program for alcohol-use disorder in young adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".