Mental Health Service Utilization by Ethiopian Immigrants and Refugees in Toronto
Bibliographic record
Abstract
The purpose of this study was to examine the mental health service utilization patterns of Ethiopians in Toronto. A cross-sectional epidemiological survey of 342 randomly selected adults was conducted, based on a conceptual model of healthcare utilization suggested by Anderson and Newman. The results suggested that 5% of the respondents sought mental health services from healthcare professionals and 8% consulted nonhealthcare professionals. Although Ethiopians' utilization rate of mental health services did not greatly differ from the rates of the general population of Ontario (6%), only a small proportion (12.5%) of Ethiopians with mental disorders used services from healthcare professionals, mostly family physicians. The data also suggested that Ethiopians were more likely to consult traditional healers than healthcare professionals for mental health problems (18.8% vs. 12.5%). In multivariate logistic regression analyses, while the number of somatic symptoms experienced was positively associated with increased mental healthcare utilization (OR = 1.515, p < 0.05), having a mental disorder was associated with decreased mental healthcare utilization (OR = 0.784, p < 0.01). Our findings have important implications for mental health services. On the one hand, the findings suggest that somatic symptoms could lead to increased use of mental health services, particularly family physicians' services. On the other hand, the data suggested that although the mental healthcare needs of Ethiopians are high, they use fewer mental health services from healthcare professionals. It would seem that family physicians could play important role in identifying and treating Ethiopian clients with somatic symptoms, as these symptoms may reflect mental disorder.
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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 | 0.001 |
| 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) | 0.001 | 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".