Disengagement from early psychosis intervention services: an observational study informed by a survey of patient and family perspectives
Bibliographic record
Abstract
Approximately one-third of patients with early psychosis disengage from services before the end of treatment. We sought to understand patient and family perspectives on early psychosis intervention (EPI) service engagement and use these findings to elucidate factors associated with early disengagement, defined as dropout from EPI in the first 9 months. Patients aged 16-29 referred to a large EPI program between July 2018-February 2020 and their family members were invited to complete a survey exploring facilitators and barriers to service engagement. A prospective chart review was conducted for 225 patients consecutively enrolled in the same EPI program, receiving the NAVIGATE model of coordinated specialty care, between July 2018-May 2019. We conducted a survival analysis, generating Kaplan-Meier curves depicting time to disengagement and Cox proportional hazards models to determine rate of disengagement controlling for demographic, clinical, and program factors. The survey was completed by 167 patients and 79 family members. The top endorsed engagement facilitator was related to the therapeutic relationship in both patients (36.5%) and families (43.0%). The top endorsed barrier to engagement was medication side effects in both patients (28.7%) and families (39.2%). In Cox proportional hazards models, medication nonadherence (HR = 2.37, 95% CI = 1.17-4.80) and use of individual psychotherapy (HR = .460, 95% CI = 0.220-0.962) were associated with early disengagement, but some of the health equity factors expected to affect engagement were not. Findings suggest that delivery of standardized treatment may buffer the effects of health disparities on service disengagement in early 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".