Special Section: A Memorial Tribute: Care of Patients With the Most Severe and Persistent Mental Illness in an Area Without a Psychiatric Hospital
Bibliographic record
Abstract
OBJECTIVE: With standard community resources, managing some patients with long-term mental illness can prove difficult, given the high level of care required. How many beds do such patients require? The study examined the prevalence, diagnostic and behavioral characteristics, and residential arrangements of a cohort of these patients in a semirural area of Canada (population of 291,500). The area has always functioned without a psychiatric hospital. METHODS: A cross-sectional inquiry was made of all relevant institutions and residential facilities (including the local jail and shelters). Key stakeholders were interviewed and provincial databases were accessed in an effort to identify all adults aged 18 to 65 originating from the catchment area who displayed both a psychotic illness and severe behavioral disturbance necessitating ongoing close supervision. The Riverview Psychiatric Inventory was used to describe and quantify behavioral problems. RESULTS: Thirty-six patients met the study criteria, for a prevalence of 12.4 per 100,000 in the general population. Most resided in a publicly funded nursing home or a well-staffed rural group home. Four (prevalence of 1.4 per 100,000) had a forensic profile, needed secure settings, and were long-term residents on acute care wards. Only one patient had transferred to a psychiatric hospital outside the catchment area. CONCLUSIONS: Care for this population can be provided outside conventional psychiatric institutions but requires highly supervised long-term residential services in the range of ten to 40 per 100,000 in the population, depending on area characteristics, with urban, socially deprived areas likely having higher needs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".