Beyond the crisis: Ongoing psychiatric treatment and service utilization after initial symptom stabilization following first-episode psychosis for adolescents
Bibliographic record
Abstract
Introduction The importance of 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. However, ongoing psychiatric treatment and service utilization after the symptoms have been stabilized over the initial years following first-episode has received less research attention. Objectives To model the variables predicting continued service utilization with psychiatrists for adolescents following their first-episode psychosis; examine associated temporal patterns in continued psychiatric service utilization. Methods This study utilized a cohort design to assess adolescents (age 14.4 ± 2.5 years) discharged following their index hospitalization for first-episode psychosis. Bivariate analyses were conducted on predictor variables associated with psychiatric service utilization. All significant predictor variables were included in a logistic regression model. Results Variables that were significantly associated with psychiatric service utilization included: diagnosis with a schizophrenia spectrum disorder rather than major mood disorder with psychotic features (OR = 24.0; P = 0.02), a first degree relative with depression (OR = 0.12; P = 0.05), and months since last psychiatric inpatient discharge (OR = 0.92; P = 0.02). Further examination of time since last hospitalization found that all adolescents continued service utilization up to 18 months post-discharge. Conclusions Key findings highlight the importance of early diagnosis, that a first degree relative with depression may negatively influence the adolescent's ongoing service utilization, and that 18 months post-discharge may a critical time to review current treatment strategies and collaborate with youth and families to ensure that services continue to meet their needs. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".