What does it mean when people say that they have received expressions of concern about their drinking or advice to cut down on the AUDIT scale?
Bibliographic record
Abstract
BACKGROUND: The Alcohol Use Disorders Identification Test (AUDIT) is a commonly used scale to measure severity of alcohol consumption that contains an item asking if anyone has expressed concern about your drinking or suggested you cut down. What does it mean when a participant says yes to this question? METHODS: Participants who were 18 or older and who drank at least weekly were recruited to complete a survey about their drinking from the Mechanical Turk platform. Comparisons were made between at risk (n = 2565) and high risk drinkers (n = 581) who said that someone had expressed concern about their drinking regarding who had expressed concern. If the person expressing concern was a health professional, the participant was also asked what type of support was provided. RESULTS: Expressions of concern about drinking were received more often by high risk than at risk drinkers. The most common type of person to have expressed concern was a relative, followed by a friend, or a marital partner. About one quarter of participants had received expressions of concern from a medical doctor or other health professional. All health professionals' expressions of concern were accompanied by a suggestion to cut down and about half provided some additional support (the most common type of support was brief advice). CONCLUSIONS: Expressions of concern come from a variety of sources and the likelihood of their occurrence is partially related to amount of alcohol intake.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".