Predicting length of stay and readmission for psychiatric inpatient youth admitted to adult mental health beds in <scp>O</scp> ntario, <scp>C</scp> anada
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to examine demographic, background, and psychopathology variables that predict length of stay and readmission among youth with mental health needs. METHOD: We analyzed data on 2445 youth who were admitted into adult psychiatric beds in Ontario, Canada. Multiple regression was used to examine length of stay, whereas logistic regression was used to examine the predictors of readmission. RESULTS: Youth were likely to stay longer in hospital if they were older, were boys, had a diagnosis of schizophrenia, mood disorders, eating disorders, personality disorders, and intellectual disability. Education, discharged against medical advice, and a diagnosis of adjustment disorders were all associated with shorter length of stay. Age, living in a group home or assisted care, a diagnosis of schizophrenia, mood disorders, and intellectual disability predicted readmission. CONCLUSION: Strategies to improve current psychiatric services (e.g. how to reduce psychiatric hospital readmissions) are discussed.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".