Dying in the shadows: the challenge of providing health care for homeless people Commentary
Bibliographic record
Abstract
The study of mortality among homeless women reported in this issue by Cheung and Hwang1 (see page 1243) is a clarion call to our society and our health care community. The stunning 10-fold disparity in mortality rates between Toronto’s homeless and housed women aged 18–44 is complemented by data from 7 other cities, which show that the risk of death among younger homeless women is 5–30 times higher than the risk among their housed counterparts. Previous studies by Hwang and others of homeless people in Boston and Toronto have reported overall mortality rates 3–5 times higher than those among the general public. 2,3 This smouldering public health crisis can no longer be ignored. Homelessness is a prism that refracts the failures of society’s
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 | 0.016 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.054 | 0.064 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".