The Association Between Income and Distress, Mental Disorders, and Suicidal Ideation and Attempts
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between household income and psychological distress, suicidal ideation and attempts, and mood, anxiety, and substance use disorders. METHOD: Data came from the Collaborative Psychiatric Epidemiology Surveys, a collection of 3 nationally representative surveys of American adults conducted between 2001 and 2003. Psychological distress, suicidal ideation, suicide attempts, and mood, anxiety, and substance use disorders were examined in relation to household income after adjusting for sex, marital status, race, age, and employment status. RESULTS: Analyses revealed an inverse association between income and psychological distress as measured by the Kessler Psychological Distress Scale, with those in the lowest income quartile demonstrating significantly more distress than any of the remaining 3 income quartiles (P < .05). Subsequent analysis of DSM-IV-diagnosed psychological disorders revealed a similar pattern of results, which were particularly strong for substance use disorders (adjusted odds ratio [AOR] = 1.74; 95% CI, 1.39-2.18), suicidal ideation (AOR = 1.77; 95% CI, 1.46-2.13), and suicide attempts (AOR = 2.15; 95% CI, 1.55-2.98). The association between income and mood and anxiety disorders was less consistent, and the relationship between income and suicidal ideation differed among the 5 race categories (non-Hispanic white, Hispanic, Asian American, black, and other). Non-Hispanic white persons showed a strong, negative relationship between income and suicidal ideation (AOR = 2.15; 95% CI, 1.66-2.80), while the association was considerably weaker or nonexistent for the other races. CONCLUSIONS: Although conclusions cannot be drawn concerning causation, the strength of associations between income, suicidal ideation, suicide attempts, and substance abuse points to the need for secondary prevention strategies among low-income, high-risk populations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".