Demographics, Health-Care Utilization, and Substance Use Profiles of Older Adults in Canada’s Lowest Income Neighbourhood
Bibliographic record
Abstract
BACKGROUND: This study aimed to determine the baseline demographics, health status, and drug use profiles of current and former substance-using older adults in Vancouver's Downtown Eastside. METHODS: Data were derived from two U.S. National Institutes of Health-funded cohort studies of current and former illegal drug users in Vancouver's Downtown Eastside. We used logistic regression of cross-sectional data obtained between June and November 2008 to calculate odds ratios and identify factors that were more commonly associated with cohort members being older adults (greater than or equal to age 50). RESULTS: 214 subjects (25%) were greater than or equal to age 50 upon study enrollment. Females (Adjusted Odds Ratio [AOR]: 0.50; 95% CI: 0.34-0.75) and individuals who reported Aboriginal ancestry (AOR: 0.49; 95% CI: 0.33-0.72) were less likely to be in the older cohort. Individuals with higher income (AOR: 2.07; 95% CI: 1.16-3.68 per $1,000), and those with a regular place to stay were more likely to be in older cohort (AOR: 3.39; 95% CI: 1.90-6.06). Older participants accessed family physicians more frequently (OR: 1.47; 95% CI: 1.01 - 2.16) and were more likely to be actively taking (OR: 3.34; 95% CI 1.71-6.55) or have taken (OR 3.21; 95% CI 1.58-6.53) HIV antiretroviral therapy. There were no differences between groups in regard to injection drug use status or daily alcohol intake. CONCLUSIONS: Older current and former illegal drug users in a major inner city Canadian centre have different demographic, health-care, and drug utilization profiles. Further studies in this population are warranted.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".