What’s the Matter? Alcohol Use Risk Among Relatives of People with Mental Illness
Bibliographic record
Abstract
Family members who live with relatives with serious mental illness face unique mental health risks, which become worse with alcohol use and without social support. Research has highlighted the damaging effects of harmful substance use among people who feel like they do not matter to others, but few studies have assessed links between mattering and alcohol use within marginalized populations. In the present study, a sample of family members who reside with a relative with mental illness completed an online survey. Using the AUDIT alcohol screening measure, participants were classified into a No–Low Risk Alcohol Use (n = 52) or a Hazardous Drinking (n = 28) group. Hazardous alcohol use was alarmingly high, reaching triple the rate of the general population and categorized at the most severe level of harm. Those who drank hazardously felt like they mattered less to others (p < 0.001), felt like they mattered less to their relative with mental illness (p = 0.035), had greater anti-mattering (e.g., they felt invisible and unheard) (p = 0.008), experienced more hopelessness (p < 0.001), felt less supported by significant others (p = 0.003), endorsed having more problems with mental health services (p = 0.017), had higher stigma (p < 0.001), and had lower psychological well-being (p < 0.001). Findings highlight under-recognized public health risks, implications for public health initiatives, and the need for tailored interventions that boost mattering and reduce harmful alcohol use in this vulnerable family member population.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".