A meta‐analysis of ethnic differences in pathways to care at the first episode of psychosis
Bibliographic record
Abstract
OBJECTIVE: We sought to systematically review the literature on ethnic differences in the likelihood of general practitioner (GP) involvement, police involvement, and involuntary admission on the pathway to care of patients with first-episode psychosis (FEP). METHOD: We searched electronic databases and conducted forward and backward tracking to identify relevant studies. We calculated pooled odds ratios (OR) to examine the variation between aggregated ethnic groups in the indicators of the pathway to care. RESULTS: We identified seven studies from Canada and England that looked at ethnic differences in GP involvement (n=7), police involvement (n=7), or involuntary admission (n=5). Aggregated ethnic groups were most often compared. The pooled ORs suggest that Black patients have a decreased likelihood of GP involvement (OR=0.70, 0.57-0.86) and an increased likelihood of police involvement (OR=2.11, 1.67-2.66), relative to White patients. The pooled ORs were not statistically significant for patients with Asian backgrounds (GP involvement OR=1.23, 0.87-1.75; police involvement OR=0.86, 0.57-1.30). There is also evidence to suggest that there may be ethnic differences in the likelihood of involuntary admission; however, effect modification by several sociodemographic factors precluded a pooling of these data. CONCLUSION: Ethnic differences in pathways to care are present at the first episode of psychosis.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".