Bibliographic record
Abstract
Full list of author information is available at the end of the articleone way to improve access, it is noted that more comprehensive harm reduction services might be needed in end-of-life care settings if they are to engage this underserved population. Background At any given moment, tens of thousands of people in Canada are homeless or marginally housed [1,2]—that is, live places unfit for human habitation (e.g., outdoors, vehicles, etc.) or temporary, transitional, or emergency accommodations (e.g., emergency shelters, hostels, etc.). Homeless and marginally housed persons have consist-ently reported levels of alcohol and/or illicit drug use many times higher than the stably housed population [3-7]. For example, a recent study of a large sample of homeless persons in Toronto found that 60 % had a life-time prevalence of regular illicit drug use and 40% reported active use of illicit drugs other than marijuana [3]. A cohort study of homeless and marginally housed youth in Vancouver reported that 41.1 % had used drugs by injection [4]. Another study of homeless women in Vancouver noted that 82.4 % of its sample regularly used
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.798 | 0.545 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".