Perceived need for care and treatment-seeking behaviour among ethnic minority groups exhibiting signs of mental illness
Bibliographic record
Abstract
AIMS: Ethnic minorities are more likely to experience negative social determinants of health, increasing their risk for mental illness. However, they are also less likely to access mental health services. This study aims to examine how perceived need for care and help-seeking behaviour (formal and informal) differ between ethnic minority groups and non-minority groups. METHODS: We analyzed respondents aged 15 years and above exhibiting signs of mental illness (past 12-month suicidal ideation, major depressive disorder, generalized anxiety disorder, social phobia and bipolar disorder) from the 2022 Mental Health and Access to Care Survey (n = 9861), a nationally representative cross-sectional survey of the Canadian population. We used modified Poisson regression analysis, with perceived need for care, formal help-seeking behaviour and informal help-seeking behaviour as outcomes, and minority status as the exposure. RESULTS: Ethnic minority groups meeting criteria for mental illness were generally less likely to perceive a need for care (prevalence ratios ranging from 0.81 to 0.90, p < 0.05 for all) and to seek help in a formal context (prevalence ratios ranging from 0.74 to 0.91, p < 0.05 for all) compared to non-ethnic minority groups. These differences were not observed for informal help-seeking, apart from ethnic minority groups who experienced symptoms of anxiety, who had reduced prevalence of seeking informal help. Gender and migrant status modified several of these relationships. CONCLUSION: People from ethnic minority groups experiencing mental illness are less likely to perceive a need for care and to seek help in formal contexts. These effects vary by gender and migrant status.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".