Are former heavy drinkers in the UK less likely to identify as being in recovery compared to those in the USA? A pilot test
Bibliographic record
Abstract
BACKGROUND: To provide a preliminary test of the prediction that fewer former heavy drinkers will identify themselves as being in recovery in the UK versus the USA. METHODS: An online cross-sectional survey was completed by a convenience sample of former heavy drinkers. This sample was identified from participants recruited to complete a questionnaire about alcohol consumption. The recruitment advertisement specified that the participants did not need to drink alcohol. The survey included items assessing self-reported current and past levels of alcohol consumption, alcohol dependence at time of heaviest alcohol consumption (ICD-10 criteria), and questions regarding identifying as currently or ever being in recovery taken from a survey by Kelly et al. (2018). RESULTS: Out of 5002 participants who completed the questionnaire, 150 were identified as former heavy drinkers from the UK or the USA. The proportion of participants reporting alcohol dependence, and the proportion of participants reporting past year abstinence, did not differ significantly between the UK and the USA (p = .841 and 0.300 respectively). Compared to participants from the UK, participants in the USA were more likely to report that they had a problem with drinking but now no longer do (24.1 % vs. 56.0 %; p < .001), and that they currently identified (4.2 % vs. 21.2 %; p = .003) or ever identified (7.4 % vs. 30.2 %; p = .001) as being in recovery. CONCLUSIONS: Identifying as being in recovery appears more common in the USA than the UK among former heavy drinkers. This apparent difference in prevalence may reflect historic differences in treatment services offered in these countries, particularly with respect to the predominance of a 12-step approach in the USA. These findings should be replicated in a representative sample.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".