Which Sociodemographic and Pathway to Care Factors Influence the Wait Time for Early Intervention for Psychosis? A Mental Health Electronic Health Records Analysis in South London
Bibliographic record
Abstract
AIM: In 2016, the Access and Waiting Time Standard (AWTS) was introduced in England, UK, outlining that people with first-episode psychosis should receive treatment from an early intervention for psychosis (EIP) service within 2 weeks. We examined sociodemographic, pathways to care (PtC), and clinical factors associated with EIP service wait time. METHOD: We collected de-identified data from a large mental health provider in South London, UK. We included patients referred and accepted to EIP services as inpatient or community contacts between 1 May 2016 and 30 April 2019, providing 3 years of data from the introduction of AWTS. Descriptive statistics and multivariable linear regression were performed. RESULTS: A total of 1806 patients were identified with a mean age of 30 (SD: 10.7) years, of whom 86.3% (n = 1559) accessed community EIP and 13.7% (n = 247) accessed inpatient EIP; of these, 26.7% were not seen within 2 weeks. Community EIP patients waited longer adj.β = 2.21 days (95% CI: 2.05-2.37) compared with inpatient EIP patients, and being older was associated with longer wait time. Conversely, a shorter wait time was associated with A&E [adj.β = -0.22 days (95% CI: -0.36, -0.10)] and 'other' [adj.β = -0.21 days (95% CI: -0.36, -0.03)] PtC characteristics. White non-British and South Asian patients had shorter wait times compared with White British patients; however, this difference diminished after adjusting for PtC and clinical factors. CONCLUSIONS: Our findings indicate that individual factors, PtC, and mode of contact influence wait time for EIP services. More than a quarter of patients were not seen within 2 weeks, indicating that targeted support in community EIP services is needed to meet clinical guidelines.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".