Bibliographic record
Abstract
AIMS: To examine the cultural impact on the diagnosis of alcohol-use disorders using European countries as examples. DESIGN: Narrative review. RESULTS: There are strong cultural norms guiding heavy drinking occasions and loss of control. These norms not only indicate what drinking behaviour is acceptable, but also whether certain behaviours can be reported or not. As modern diagnostic systems are based on lists of mostly behavioural criteria, where alcohol-use disorders are defined by a positive answer on at least one, two or three of these criteria, culture will inevitably co-determine how many people will get a diagnosis. This explains the multifold differences in incidence and prevalence of alcohol-use disorders, even between countries where the average drinking levels are similar. Thus, the incidence and prevalence of alcohol-use disorders as assessed by surveys or rigorous application of standardised instruments must be judged as measuring social norms as well as the intended mental disorder. CONCLUSIONS: Current practice to measure alcohol-use disorders based on a list of culture-specific diagnostic criteria results in incomparability in the incidence, prevalence or disease burden between countries. For epidemiological purposes, a more grounded definition of diagnostic criteria seems necessary, which could probably be given by using heavy drinking over time.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".