Association between Food Insecurity and Procurement Methods among People Living with HIV in a High Resource Setting
Bibliographic record
Abstract
OBJECTIVE: People living with HIV in high-resource settings suffer severe levels of food insecurity; however, limited evidence exists regarding dietary intake and sub-components that characterize food insecurity (i.e. food quantity, quality, safety or procurement) in this population. We examined the prevalence and characteristics of food insecurity among people living with HIV across British Columbia, Canada. DESIGN: This cross-sectional analysis was conducted within a national community-based research initiative. METHODS: Food security was measured using the Health Canada Household Food Security Scale Module. Logistic regression was used to determine key independent predictors of food insecurity, controlling for potential confounders. RESULTS: Of 262 participants, 192 (73%) reported food insecurity. Sub-components associated with food insecurity in bivariate analysis included: < RDI consumption of protein (p = 0.046); being sick from spoiled/unsafe food in the past six months (p = 0.010); and procurement of food using non-traditional methods (p <0.05). In multivariable analyses, factors significantly associated with food insecurity included: procurement of food using non-traditional methods [AOR = 11.11, 95% CI: 4.79-25.68, p = <0.001]; younger age [AOR = 0.92, 95% CI: 0.86-0.96, p = <0.001]; unstable housing [AOR = 4.46, 95% CI: 1.15-17.36, p = 0.031]; household gross annual income [AOR = 4.49, 95% CI: 1.74-11.60, p = 0.002]; and symptoms of depression [AOR = 2.73, 95% CI: 1.25-5.96, p = 0.012]. CONCLUSIONS: Food insecurity among people living with HIV in British Columbia is characterized by poor dietary quality and food procurement methods. Notably, participants who reported procuring in non-traditional manners were over 10 times more likely to be food insecure. These findings suggest a need for tailored food security and social support interventions in this setting.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".