Hunger and associated harms among injection drug users in an urban Canadian setting
Bibliographic record
Abstract
BACKGROUND: Food insufficiency is often associated with health risks and adverse outcomes among marginalized populations. However, little is known about correlates of food insufficiency among injection drug users (IDU). METHODS: We conducted a cross-sectional study to examine the prevalence and correlates of self-reported hunger in a large cohort of IDU in Vancouver, Canada. Food insufficiency was defined as reporting "I am hungry, but don't eat because I can't afford enough food". Logistic regression was used to determine independent socio-demographic and drug-use characteristics associated with food insufficiency. RESULTS: Among 1,053 participants, 681 (64.7%) reported being hungry and unable to afford enough food. Self-reported hunger was independently associated with: unstable housing (adjusted odds ratio [AOR]: 1.68, 95% confidence interval [CI]: 1.20 - 2.36, spending ≥ $50/day on drugs (AOR: 1.43, 95% CI: 1.06 - 1.91), and symptoms of depression (AOR: 3.32, 95% CI: 2.45 - 4.48). CONCLUSION: These findings suggest that IDU in this setting would likely benefit from interventions that work to improve access to food and social support services, including addiction treatment programs which may reduce the adverse effect of ongoing drug use on hunger.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".