Specialized assertive community treatment intervention for homeless youth with first episode psychosis and substance use disorder: A 2‐year follow‐up study
Bibliographic record
Abstract
AIM: No previous study has investigated interventions for homeless youth suffering from first episode psychosis and comorbid substance use disorder (HYPS). An intensive assertive community intervention team (IACIT) offering outreach interventions, housing support as well as integrated care for early psychosis and substance use disorder (SUD) was created in 2012 at the Centre Hospitalier de l'Université de Montréal (CHUM). To explore the impact of the addition of an IACIT to an early intervention for psychosis service (EIS) on housing stability, functional and symptomatic outcomes and mental health service use. METHODS: A two-year longitudinal study comparing the outcome of HYPS receiving combined EIS and IACIT since 2012, to a historical cohort of HYPS receiving EIS only between 2005 and 2011. Socio-demographic data, housing stability, functioning, illness severity, SUD severity, emergency room visits and hospitalizations were assessed at admission, at 1 month, and every 3 months thereafter. RESULTS: HYPS receiving EIS + IACIT achieved housing stability more rapidly and spent less time hospitalized than HYPS getting EIS only (RR 2.38, P = .017). HYPS with cocaine misuse were less likely to attain housing stability (RR 0.25, P = .04). No between-group differences were found for psychiatric symptoms, functioning and SUD outcomes. CONCLUSION: The addition of IACIT-HYPS to EIS was associated with earlier housing stability and reduced total hospitalization days compared to EIS alone.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".