The impact of describing someone as being in recovery from alcohol problems on the general public’s beliefs about their life, use of treatment, and drinking status
Bibliographic record
Abstract
Purpose: The general public’s attitudes toward former heavy drinkers can impact on the wellbeing of these individuals. The current study sought to determine if describing a former heavy drinker as ‘in recovery,’ and varying the amount they drank, impacts the general public’s perceptions of how the person is functioning (both personally and as a member of society), their need for treatment, and the possibility of a moderate drinking recovery.Materials and methods: An online panel survey (n = 4450; adults from multiple countries) asked participants to read a brief vignette describing a former heavy drinker (i.e. John). Participants were randomized to receive a vignette in which John was described as ‘in recovery’ (vs. no mention of recovery) and as having consumed heavy (vs. very heavy) amounts of alcohol prior to seeking help. Participants were then asked to rate John on how he is functioning, and to also rate the possibility of his recovery with or without treatment and as abstinent or a current moderate drinker.Results and Conclusions: Participants who read the vignette in which John was described as being in recovery rated him as being more likely to be functioning well compared to those where no mention of ‘recovery’ was made. However, this manipulation did not impact ratings regarding the likelihood of untreated and moderate drinking recoveries. Varying the amount of drinking described did not impact ratings of how John was functioning but very heavy (compared to heavy) drinking reduced ratings of the likelihood of untreated and moderate drinking recoveries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".