The pathways to mental health care of first-episode psychosis patients: a systematic review
Bibliographic record
Abstract
BACKGROUND: Although there is agreement on the association between delay in treatment of psychosis and outcome, less is known regarding the pathways to care of patients suffering from a first psychotic episode. Pathways are complex, involve a diverse range of contacts, and are likely to influence delay in treatment. We conducted a systematic review on the nature and determinants of the pathway to care of patients experiencing a first psychotic episode.MethodWe searched four databases (Medline, HealthStar, EMBASE, PsycINFO) to identify articles published between 1985 and 2009. We manually searched reference lists and relevant journals and used forward citation searching to identify additional articles. Studies were included if they used an observational design to assess the pathways to care of patients with first-episode psychosis (FEP). RESULTS: Included studies (n=30) explored the first contact in the pathway and/or the referral source that led to treatment. In 13 of 21 studies, the first contact for the largest proportion of patients was a physician. However, in nine of 22 studies, the referral source for the greatest proportion of patients was emergency services. We did not find consistent results across the studies that explored the sex, socio-economic, and/or ethnic determinants of the pathway, or the impact of the pathway to care on treatment delay. CONCLUSIONS: Additional research is needed to understand the help-seeking behavior of patients experiencing a first-episode of psychosis, service response to such contacts, and the determinants of the pathways to mental health care, to inform the provision of mental health services.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".