High prevalence of food insecurity among HIV-infected individuals receiving HAART in a resource-rich setting
Bibliographic record
Abstract
This study aimed to assess the prevalence and correlates of food insecurity in a cohort of HIV-infected individuals on highly active antiretroviral therapy (HAART) in British Columbia (BC), Canada. Adults receiving HAART voluntarily enrolled into the Longitudinal Investigations into Supportive and Ancillary Health Services (LISA) cohort. Individual food insecurity was measured using a modified version of the Radimer/Cornell Questionnaire. We performed bivariate analyses to determine differences between explanatory variables for individuals who were food secure and food insecure. We performed logistic regression to determine independent predictors of food insecurity. Of the 457 individuals enrolled in the LISA cohort, 324 (71.0%) were found to be food insecure. Multivariate analysis indicated that individuals who had an annual incomes less than $15,000 (odds ratio [OR] 3.15, 95% confidence interval [CI] 1.83, 5.44), used illicit drugs (OR 1.85, 95% CI 1.03, 3.33), smoked tobacco (OR 2.30, 95% CI 1.30, 4.07), had depressive symptoms (OR 2.34, 95% CI 1.38, 3.96), and were younger (OR 0.95, 95% CI, 0.92, 0.98) were more likely to be food insecure. Our results demonstrated a high (71%) prevalence of food insecurity among HIV-infected individuals receiving HAART in this resource-rich setting, and that food insecurity is associated with a compendium of environmental and behavioral factors. More research is needed to understand the biological and social pathways linking food insecurity to these variables in order to identify program strategies that can effectively improve food security among HIV-infected populations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".