Predictors of Medical or Surgical and Psychiatric Hospitalizations Among a Population-Based Cohort of Homeless Adults
Bibliographic record
Abstract
OBJECTIVES: We identified factors associated with inpatient hospitalizations among a population-based cohort of homeless adults in Toronto, Ontario. METHODS: We recruited participants from shelters and meal programs. We then linked them to administrative databases to capture hospital admissions during the study (2005-2009). We used logistic regression to identify predictors of medical or surgical and psychiatric hospitalizations. RESULTS: Among 1165 homeless adults, 20% had a medical or surgical hospitalization, and 12% had a psychiatric hospitalization during the study. These individuals had a total of 921 hospitalizations, of which 548 were medical or surgical and 373 were psychiatric. Independent predictors of medical or surgical hospitalization included birth in Canada, having a primary care provider, higher perceived external health locus of control, and lower health status. Independent predictors of psychiatric hospitalization included being a current smoker, having a recent mental health problem, and having a lower perceived internal health locus of control. Being accompanied by a partner or dependent children was protective for hospitalization. CONCLUSIONS: Health care need was a strong predictor of medical or surgical and psychiatric hospitalizations. Some hospitalizations among homeless adults were potentially avoidable, whereas others represented an unavoidable use of health services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".