Patient and Contextual Factors Related to the Decision to Hospitalize Patients From Emergency Psychiatric Services
Bibliographic record
Abstract
OBJECTIVE: Mental health care reform has brought an increasing emphasis on community care, with concomitant reductions in inpatient psychiatric resources. Hospitalization remains a necessary and integral component of the mental health care system, but it is taking on a more specialized role. Examining the circumstances in which hospitalization is indicated can help clarify emergency psychiatric practices and determine whether patients' needs are being met within this changing environment. This pilot study examined the impact of selected patient and contextual characteristics on the decision to admit patients to inpatient psychiatric units and assessed the utility of the Severity of Psychiatric Illness (SPI) scale for monitoring clinical practice in emergency psychiatric services. METHODS: Crisis workers in two emergency psychiatric services crisis teams in Toronto, Canada, used the SPI in the assessment of 205 visitors to the services during the winter of 1998-1999. Contextual characteristics, including bed availability, service site, and the admitting physician's level of training, were recorded. Multivariate logistic regression was used to assess the relative contribution of patient and contextual variables in the admission decision. RESULTS: The severity of axis I symptoms and difficulties with self-care were significantly associated with the decision to admit. Site, bed availability, and the admitting physician's level of training did not appear to be associated with clinical decisions. CONCLUSIONS: Patients with the most need are being admitted to inpatient units despite significant systemic pressures on inpatient services. The SPI is a useful and discriminating tool for evaluating clinical practice in emergency services.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".