Bibliographic record
Abstract
CONTEXT: Homeless persons in US cities have high mortality rates. However, few comparison data exist for death rates among homeless persons in other developed countries. OBJECTIVES: To compare mortality rates among men using homeless shelters and the general population in Toronto, Ontario, and to determine whether mortality rates differ significantly among men using homeless shelters in Canadian and US cities. DESIGN: Cohort study conducted from 1995 through 1997, with a mean follow-up of 2.6 years. PARTICIPANTS: Men aged 18 years or older who used homeless shelters in Toronto in 1995 (n=8933). MAIN OUTCOME MEASURE: Mortality rate ratios comparing age-specific mortality rates among men using homeless shelters in Toronto with those of men in the general population of Toronto and of men using homeless shelters in New York, NY; Boston, Mass; and Philadelphia, Pa. RESULTS: Men using homeless shelters in Toronto were more likely to die than men in the city's general population. Mortality rate ratios were 8.3 (95% confidence interval [CI], 4.4-15.6) for men aged 18 to 24 years, 3.7 (95% CI, 3.0-4.6) for men aged 25 to 44 years, and 2.3 (95% CI, 1.8-3.0) for men aged 45 to 64 years. In most cases, however, the risk of death was significantly lower for men using homeless shelters in Toronto than for those in US cities. For men aged 25 to 44 years using homeless shelters, mortality rate ratios were 0.52 (95% CI, 0.41-0.65) for Toronto compared with Boston and 0.61 (95% CI, 0.44-0.85) for Toronto compared with New York City. For men aged 35 to 54 years using homeless shelters, the mortality rate ratio was 0.42 (95% CI, 0.27-0.66) for Toronto compared with Philadelphia. CONCLUSIONS: Mortality rates among men who use homeless shelters in Toronto, while higher than in the general population of Toronto, are much lower than mortality rates observed among men using homeless shelters in 3 major US cities. Further study is needed to identify the reasons for this disparity.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".