The Close Relatives of Untreated Heavy Drinkers: Perspectives on Heavy Drinking and its Effects
Bibliographic record
Abstract
Aims. To describe the positions adopted by close family members of a community sample of untreated heavy drinkers. Design. Detailed interview and questionnaire study of a sample of close family members and the heavy drinkers to whom they were related. Sample. 50 close relatives of 50 heavy drinkers drawn from a community cohort of 500 in the English West Midlands. Data. Perceived benefits and drawbacks of drinking checklist (family members and heavy drinkers); readiness to change questionnaire (family members and heavy drinkers); coping questionnaire (family members only); semi-structured interview (family members only). Findings. A wide range of positions towards their relatives' heavy drinking was evident in this sample of family members. Most recognised at least some drawbacks to their relatives' drinking, and some were engaged in efforts to change it. A number of impediments to taking a stand about their relatives' drinking were apparent, including recognising the benefits of drinking, mitigating factors, the wish not to be intolerant, others' support for their relatives' drinking, and sometimes the family member's own heavy drinking. Conclusions. Some light has been thrown on the experiences of a hitherto neglected population of family members, who may face a number of difficulties and dilemmas in deciding how to respond in the face of heavy drinking.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| 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".