Drug problems among homeless individuals in Toronto, Canada: prevalence, drugs of choice, and relation to health status
Bibliographic record
Abstract
BACKGROUND: Drug use is believed to be an important factor contributing to the poor health and increased mortality risk that has been widely observed among homeless individuals. The objective of this study was to determine the prevalence and characteristics of drug use among a representative sample of homeless individuals and to examine the association between drug problems and physical and mental health status. METHODS: Recruitment of 603 single men, 304 single women, and 284 adults with dependent children occurred at homeless shelters and meal programs in Toronto, Canada. Information was collected on demographic characteristics and patterns of drug use. The Addiction Severity Index was used to assess whether participants suffered from drug problems. Associations of drug problems with physical and mental health status (measured by the SF-12 scale) were examined using regression analyses. RESULTS: Forty percent of the study sample had drug problems in the last 30 days. These individuals were more likely to be single men and less educated than those without drug problems. They were also more likely to have become homeless at a younger age (mean 24.8 vs. 30.9 years) and for a longer duration (mean 4.8 vs. 2.9 years). Marijuana and cocaine were the most frequently used drugs in the past two years (40% and 27%, respectively). Drug problems within the last 30 days were associated with significantly poorer mental health status (-4.9 points, 95% CI -6.5 to -3.2) but not with poorer physical health status (-0.03 points, 95% CI -1.3 to 1.3)). CONCLUSIONS: Drug use is common among homeless individuals in Toronto. Current drug problems are associated with poorer mental health status but not with poorer physical health status.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".