Alcohol use disorder severity and reported reasons not to seek treatment: a cross-sectional study in European primary care practices
Bibliographic record
Abstract
BACKGROUND: Alcohol use disorders are among the mental disorders with the lowest treatment rates. Increasing the treatment rates requires insight on the reasons why patients do not seek treatment. This study examined self-reported reasons for not seeking treatment and their association with alcohol use disorder severity among primary health care patients diagnosed with an alcohol use disorder. METHODS: Alcohol use disorders, health service utilization, and reasons for not seeking treatment were assessed via interviews on regionally representative samples of primary care patients from 6 European countries (Italy, Germany, Hungary, Latvia, Poland and Spain, total N = 9,098). Additionally, general practitioners had to fill in a questionnaire assessing their patients' alcohol use and alcohol use disorders. A multinomial logistic regression was performed to investigate the association between reasons for not seeking treatment and alcohol use disorder severity. RESULTS: Of 1,008 patients diagnosed with an alcohol use disorder (via general practitioner or patient interview) in the past 12 months, the majority (N = 810) did not receive treatment and 251 of those gave a reason for not seeking treatment. The most frequent reason was 'lack of problem awareness' (55.3% of those who responded), the second most common response was 'stigma or shame' (28.6%), followed by 'encounter barriers' (22.8%) and 'cope alone' (20.9%). The results indicated lower probabilities of reporting 'denial' and higher probabilities to report 'encounter barriers' as alcohol use disorders severity increases. However, both trends were discontinued for patients with severe alcohol use disorders. CONCLUSIONS: Particularly at lower levels of alcohol use disorder severity, a lack of problem awareness prevents patients from seeking treatment. Routinely alcohol consumption monitoring in primary care practices could help primary and secondary prevention of alcohol use disorders and increase treatment coverage.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 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".