Predictors of Psychiatric Aftercare among Formerly Hospitalized Adolescents
Bibliographic record
Abstract
OBJECTIVE: Timely aftercare can be viewed as a patient safety imperative. In the context of decreasing inpatient length of stay (LOS) and known child psychiatry human resource challenges, we investigated time to aftercare for adolescents following psychiatric hospitalization. METHOD: We conducted a population-based cohort study of adolescents aged 15 to 19 years with psychiatric discharge between April 1, 2002, and March 1, 2004, in Ontario, using encrypted identifiers across health administrative databases to determine time to first psychiatric aftercare with a primary care physician (PCP) or a psychiatrist within 395 days of discharge. RESULTS: Among the 7111 adolescents discharged in the study period, 24% had aftercare with a PCP or a psychiatrist within 7 days and 49% within 30 days. High socioeconomic status (adjusted hazard ratio [AHR] 1.31; 95% CI 1.21 to 1.43, P < 0.001) and psychotic disorders (AHR 1.24; 95% CI 1.12 to 1.36, P < 0.001) were associated with greater likelihood of aftercare. Youth in the northern part of the province (AHR 0.48; 95% CI 0.32 to 0.71, P < 0.001), rural areas (AHR 0.82; 95% CI 0.76 to 0.89, P < 0.001), and with self-harm or suicide attempts (AHR 0.58; 95% CI 0.53 to 0.64, P < 0.001) and substance use disorders (AHR 0.50; 95% CI 0.44 to 0.56, P < 0.001) were less likely to receive aftercare. CONCLUSIONS: Hospitalization is our most intensive, intrusive, and expensive psychiatric treatment setting, yet in our cohort of formerly hospitalized adolescents fewer than 50% received psychiatry-related aftercare in the month postdischarge. Innovations are necessary to address geographic inequities and improve timely access to mental health aftercare for all youth.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".