High Utilizers of Emergency Health Services in a Population-Based Cohort of Homeless Adults
Bibliographic record
Abstract
OBJECTIVES: We identified predictors of emergency department (ED) use among a population-based prospective cohort of homeless adults in Toronto, Ontario. METHODS: We assessed ED visit rates using administrative data from the Institute for Clinical Evaluative Sciences (2005-2009). We then used logistic regression to identify predictors of ED use. Frequent users were defined as participants with rates in the top decile (≥ 4.7 visits per person-year). RESULTS: Among 1165 homeless adults, 892 (77%) had at least 1 ED visit during the study. The average rate of ED visits was 2.0 visits per person-year, whereas frequent users averaged 12.1 visits per person-year. Frequent users accounted for 10% of the sample but contributed more than 60% of visits. Predictors of frequent use in adjusted analyses included birth in Canada, higher monthly income, lower health status, perceived unmet mental health needs, and perceived external health locus of control from powerful others; being accompanied by a partner or dependent children had a protective effect on frequent use. CONCLUSIONS: Among homeless adults with universal health insurance, a small subgroup accounted for the majority of visits to emergency services. Frequent use was driven by multiple predisposing, enabling, and need factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".