The Impact of Realigning a Tertiary Psychiatric Hospital in British Columbia on Other Institutional Sectors
Bibliographic record
Abstract
OBJECTIVE: Deinstitutionalization is an ongoing process, as many jurisdictions continue to struggle with redesigning their psychiatric systems. Historically, reducing psychiatric beds and closing hospitals have resulted in deleterious outcomes for people with severe and persistent mental illness. More recent evidence suggests that careful implementation of deinstitutionalization policies can thwart potential adverse consequences and may even foster favorable outcomes. This study evaluated the extent to which the recent devolution of the only tertiary psychiatric hospital in British Columbia resulted in a direct shift of individuals to other institutional sectors, such as criminal justice and health sectors. METHODS: Admission rates to general hospitals, continuing care facilities, correctional institutions, and forensic psychiatric facilities were compared among two patient groups: those discharged before the realignment of the tertiary psychiatric hospital system (prerealignment cohort) (N=164) and those discharged after initiation of the system reforms (postrealignment cohort) (N=171). RESULTS: Most of the patients in the postrealignment cohort have remained in the tertiary care settings to which they were originally discharged. For patients in the postrealignment cohort, contact with other institutional sectors was rare and shorter in duration than it was for patients in the prerealignment cohort. CONCLUSIONS: This study provides preliminary evidence that recent efforts to realign British Columbia's provincial tertiary psychiatric hospital system have not resulted in a significant shift of the relocated patients to institutions in other sectors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".