Pathways to early intervention services involving police or ambulance and disengagement rates in racialized and immigrant youth compared to the White majority
Bibliographic record
Abstract
OBJECTIVES: Early-intervention services (EIS) are the gold standard for first-episode psychosis (FEP). Immigrants and racialized youth with FEP are more likely to access EIS through adverse pathways (police and/or ambulance-mediated) and to disengage from treatment. We aimed to use a comprehensive, intersectional approach to examine inequalities in pathways to care and EIS disengagement, comparing immigrants stratified by generational and racialized minority status, with White majority non-immigrants. METHODS: Incident FEP cases from two Canadian EIS were stratified according to immigrant generation and racialized minority status. Pathways to care and disengagement were examined using logistic regression models adjusted for potential confounders. RESULTS: Of 567 participants, 173 (30.8 %) experienced an adverse pathway. The proportion of adverse pathway was highest among racialized first-generation immigrants (N = 52, 38.2 %), followed by non-racialized (N = 13, 32.5 %) and racialized second-generation immigrants (N = 23, 32.4 %). White non-immigrants (N = 77, 26.9 %) and non-racialized first-generation immigrants (N = 6, 23.1 %) presented lower rates. Odds of adverse pathways were only significantly increased for racialized first-generation immigrants (OR = 1.72, 95 % CI = 1.06-2.80) compared with White non-immigrants. Disengagement was associated with adverse pathways to care (OR = 1.80, 95 % CI = 1.09-2.97), but not with immigrant stratified groups. The relationship between adverse pathways and disengagement was not moderated by immigrant status. CONCLUSIONS: Racialized first-generation immigrants are more likely to encounter adverse pathways. The interplay between systemic, cultural, and individual factors, including perceived need for mental healthcare knowledge and trust towards healthcare institutions can hinder access to services. Racially differing practices in emergency response may play a role. Further research is needed to understand inequities in EIS access.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".