Furthering understanding of the scope and variation of alcohol and drug harms to others: Using qualitative discussion groups to inform survey development
Bibliographic record
Abstract
Aims: Alcohol and drug use can have negative effects on family and friends of someone who uses these substances. To give voice to people with lived experience, we sought in-depth qualitative data from people who experienced such harms to others (HTO) to better understand the scope and variation of alcohol and drug HTO to inform future survey research in the United States (US). Design: Five discussion groups with people from varied racial and ethnic groups. Setting: Five US cities with different sociodemographic profiles and alcohol and drug use patterns. Participants: Family members of individuals with substance use disorders (SUD). Measures: Thematic analysis was used to identify themes and highlight harms that have not been well-represented in US general population surveys to date. Findings: Discussion group participants described how alcohol and drug HTO can have long-lasting effects, raising questions about strategies to query and document harms occurring over the lifecourse. The emotional stress and burden of a close relationship with someone with SUD was a recurrent theme. Participants also noted how systems and policies may inadvertently intensify HTO through negative interactions with legal or social service entities. They also identified helpful community resources (including Al-Anon) for people impacted by someone else's substance use. Conclusions: Qualitative data from people with relevant lived experience identified new areas for alcohol and other drug HTO research, including duration of harms across the lifespan, emotional and psychological impacts, and systems-level harms. Findings informed a redesign of our national survey instrument to efficiently capture the broad range of HTO.
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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 | 0.189 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".