First‐episode psychosis: Ongoing mental health service utilization during the stable period for adolescents
Bibliographic record
Abstract
AIM: The timely identification and treatment of psychosis are increasingly the focus of early interventions, with research targeting the initial high-risk period in the months following first-episode hospitalization. Ongoing treatment after stabilization is also essential in the years following a first-episode psychosis (FEP), but has received less research attention. In this study, variables that could impact continued psychiatric service utilization by adolescents following their FEP and temporal patterns in service utilization are examined. METHODS: Families of 52 adolescents (aged 14.4 ± 2.5 years) discharged following a hospitalization for FEP were contacted two or more years following the adolescents' discharge. A chart review (Time 1) of hospital records provided clinical data on each adolescent's psychiatric diagnosis, symptoms, illness course, medications and family history. Follow-up (Time 2) data were collected from parents/caregivers using a questionnaire enquiring about post-discharge treatment history and service utilization. RESULTS: Bivariate analyses were conducted to identify Time 1 variables associated with psychiatric service utilization at Time 2. Significant variables were included in a logistic regression model and three variables were independently associated with continued service utilization: having a primary diagnosis of schizophrenia (odds ratio (OR) = 24.0; P = 0.02), not having a first-degree relative with depression (OR = 0.12; P = 0.05) and fewer months since the last inpatient discharge (OR = 0.92; P = 0.02). CONCLUSIONS: Findings suggest: (1) the importance of early diagnosis, (2) that a relative with depression may negatively influence the adolescent's ongoing service utilization, and (3) that 18 months post-discharge may be a critical time to review treatment strategies and collaborate with youth and families to ensure appropriateness of 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".