Ethnic Diversity and Pathways to Care for a First Episode of Psychosis in Ontario
Bibliographic record
Abstract
OBJECTIVE: To examine ethnic variations in the pathways to care for persons accessing early intervention (EI) services in Ontario. METHOD: The pathways to care and the duration of untreated psychosis were assessed for first-episode psychosis patients who entered specialized EI services in Ontario. The sample was assigned to the following ethnic classifications: the White (Caucasian), Black (African descent), and Asian (ancestry from the continent) groups, plus all the "other ethnicities" group. RESULTS: There were 200 participants: 78% were male; 61% from the White, 15% Black, 13% Asian, and 11% were from the other ethnicities group. At the first point of contact, more participants used nonmedical contacts (12%), such as clergy and naturopathic healers, than psychologists (8%) or psychiatrists (7%). There were no ethnic differences for duration of untreated psychosis (median 22 weeks) or for initiation of help seeking by family/friends (53%), police (15%), or self (33%). After adjusting for relevant clinical and demographic factors, the Asian and other ethnicities groups were 4 and 3 times (respectively) more likely than the White or Black groups (P = .017) to use emergency room services as the first point of contact in the pathways to care. Participants from the Asian group experienced less involuntary hospitalizations (P = .023) than all the other groups. Yet overall, there were many more similarities than significant differences in the pathways to care. CONCLUSION: EI services should monitor the pathways to care for young people of diverse ethnic backgrounds to address any disparities in accessing care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".