Racial-Ethnic Differences in Health Service Use in a Large Sample of Homeless Adults With Mental Illness From Five Canadian Cities
Bibliographic record
Abstract
OBJECTIVE: This study examined factors associated with health care use in an ethnically diverse Canadian sample of homeless adults with mental illness, a particularly disadvantaged group. METHODS: Baseline survey data were available from five sites across Canada for 2,195 At Home/Chez Soi demonstration project participants. Negative binomial regression models examined the relationship between racial-ethnic or cultural group membership (white, N=1,085; Aboriginal, N=476; black, N=244; and other ethnoracial minority groups, N=390) and self-reported emergency department (ED) visits and hospitalizations in the past six months and past-month visits to a medical, other clinical, or social service provider. Adjusted models included other predisposing, enabling, and need factors, based on Andersen's behavioral model for vulnerable populations. RESULTS: Compared with white participants, black participants had a lower rate of ED visits (adjusted rate ratio [ARR]=.54, 95% confidence interval [CI]=.43-.69) and Aboriginal participants had a lower rate of medical visits (ARR=.84, CI=.71-.99) and a higher rate of visits to social service providers (ARR=1.54, CI=1.18-2.01). Participants in other ethnoracial minority groups had a higher rate of social service provider visits than white participants (ARR=1.44, CI=1.10-1.89). Access to a family physician, having at least high school education, and high needs for mental health services were associated with greater use of ED and medical visits and hospitalizations. Rates of ED and medical visits were lower with increased age and better physical health. CONCLUSIONS: In a system of universal health insurance that prioritizes access to and quality of care, the presence of racial-ethnic disparities experienced by this vulnerable population merits further attention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".