Food insecurity during the COVID-19 pandemic who use drugs in Vancouver, Canada
Bibliographic record
Abstract
OBJECTIVE: To examine prevalence and factors associated with food insecurity among people who use drugs (PWUD) during the first year of the COVID-19 pandemic and the overdose crisis. DESIGN: This cross-sectional study employs multivariable logistic regression to identify factors associated with self-reported food insecurity. PARTICIPANTS: PWUD who are part of three community-recruited cohorts. SETTING: Interviews conducted in Vancouver, Canada, via phone between July and November 2020 in adherence to COVID-19 safety procedures. RESULTS: Among 765 participants, including 433 (56·6 %) men, eligible for this study, 146 (19·1 %; 95 % CI: 16·3 %, 21·9 %) reported food insecurity in the past month. Of the participants reporting food insecurity, 114 (78·1 %) reported that their hunger levels had increased since the beginning of the pandemic. In multivariable analyses, factors independently and positively associated with food insecurity included: difficulty accessing health or social services (adjusted OR (AOR) = 2·59; 95 % CI: 1·60, 4·17); having mobility difficulties (AOR = 1·59; 95 % CI: 1·02, 2·45) and engaging in street-based income generation (e.g. panhandling and informal recycling) (AOR = 2·31; 95 % CI: 1·45, 3·65). CONCLUSION: Approximately one in five PWUD reported food insecurity during this time. PWUD with mobility issues, who experienced difficulty accessing services and/or those engaged in precarious street-based income generation were more likely to report food insecurity. Food security is paramount to the success of interventions to prevent COVID-19 and drug toxicity deaths. These findings suggest a need for a more unified state response to food insecurity that prioritises and incorporates accessibility and autonomy of the communities they serve.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".