Socioeconomic Status and Self-Reported Barriers to Mental Health Service Use
Bibliographic record
Abstract
OBJECTIVE: Socially disadvantaged individuals are at high risk for having their mental health service needs unmet. We explored the relations among education level, income level, and self-reported barriers to mental health service use for individuals with a mental illness, using data from the Canadian Community Health Survey: Mental Health and Well-Being (CCHS 1.2). METHODS: Our sample group comprised the 8.3% of adult respondents who met the CCHS 1.2 criteria for an anxiety or affective disorder in the past 12 months (n = 3101). We examined the association between education and income levels and self-reported accessibility, availability, or acceptability barriers to mental health care. RESULTS: Accessibility, availability, and acceptability barriers were reported by 3%, 5%, and 16% of our sample, respectively. Individuals with a high school diploma were less likely than individuals without a high school diploma to report acceptability barriers to care (odds ratio 0.65; 95% confidence interval, 0.45 to 0.93). Higher-income individuals were less likely than lower-income individuals to report acceptability barriers to care (odds ratio 0.69; 95% confidence interval, 0.50 to 0.96). Employment, distress level, age, and family structure were also associated with acceptability barriers. CONCLUSION: Issues related to acceptability explain most of the unmet need for mental health services. Program planners should consider the development of targeted approaches to service delivery and outreach for low-income, working individuals who have not completed high school.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".