Developing Pilot Interventions to Address Food Insecurity and Nutritional Needs of People Living With HIV in Latin America and the Caribbean: An Interinstitutional Approach Using Formative Research
Bibliographic record
Abstract
BACKGROUND: Food insecurity and malnutrition present challenges to HIV management, but little research has been done in Latin America and the Caribbean (LAC). OBJECTIVE: To assess levels of food insecurity and malnutrition among people living with HIV (PLHIV) across multiple countries in LAC to inform pilot projects and policy. METHODS: Through interinstitutional collaboration, we collected data on sociodemographics, household food security, anthropometry, and commonly consumed foods among adults seeking care at HIV clinics in Bolivia, Honduras, and the Dominican Republic (DR; N = 400) and used the results for pilot projects. RESULTS: Most PLHIV had moderate or severe household food insecurity (61% in Bolivia, 71% in Honduras, and 68% in DR). Overweight and obesity were also highly prevalent, particularly among women (41%-53% had body mass index ≥25). High body fat was also prevalent, ranging from 36% to 59%. Among salient foods, fruits and vegetables were lacking. Country-specific pilot projects incorporated locally tailored nutrition counseling with a monthly household food ration, linkage to income-generating projects, or urban gardens. Nutritional counseling was conducted initially by professionals and later modified for peer counselors given the lack of nutritionists. CONCLUSION: High levels of food insecurity and overweight among PLHIV in LAC have important implications, since prior interventions to address food insecurity among PLHIV have focused on underweight and wasting. Formative research and intersectoral collaboration facilitated locally appropriate nutritional materials and interventions, enhanced local capacities, and helped incorporate nutritional guidelines into policies and practice. Addressing human capital constraints in resource-poor settings and developing complementary strategies were key recommendations.
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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 | 0.070 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".