A Comprehensive Assessment of Health Care Utilization Among Homeless Adults Under a System of Universal Health Insurance
Bibliographic record
Abstract
OBJECTIVES: We comprehensively assessed health care utilization in a population-based sample of homeless adults and matched controls under a universal health insurance system. METHODS: We assessed health care utilization by 1165 homeless single men and women and adults in families and their age- and gender-matched low-income controls in Toronto, Ontario, from 2005 to 2009, using repeated-measures general linear models to calculate risk ratios and 95% confidence intervals (CIs). RESULTS: Homeless participants had mean rates of 9.1 ambulatory care encounters (maximum = 141.1), 2.0 emergency department (ED) encounters (maximum = 104.9), 0.2 medical-surgical hospitalizations (maximum = 14.9), and 0.1 psychiatric hospitalizations per person-year (maximum = 4.8). Rate ratios for homeless participants compared with matched controls were 1.76 (95% CI = 1.58, 1.96) for ambulatory care encounters, 8.48 (95% CI = 6.72, 10.70) for ED encounters, 4.22 (95% CI = 2.99, 5.94) for medical-surgical hospitalizations, and 9.27 (95% CI = 4.42, 19.43) for psychiatric hospitalizations. CONCLUSIONS: In a universal health insurance system, homeless people had substantially higher rates of ED and hospital use than general population controls; these rates were largely driven by a subset of homeless persons with extremely high-intensity usage of health services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".