Food insecurity and unemployment as mediators of the relationship between the COVID-19 pandemic and psychological well-being in young South Africans with HIV
Bibliographic record
Abstract
BACKGROUND: Poor psychological well-being, including depression, anxiety, and low self-esteem, is both prevalent among young South Africans living with HIV and associated with poor HIV clinical outcomes. By impacting food insecurity and employment, the COVID-19 pandemic may have influenced psychological well-being in this population. This analysis sought to examine whether food insecurity and unemployment mediated the relationship between study cohort (pre- versus during-pandemic) and psychological well-being in our sample of young South Africans living with HIV. METHODS: This was a secondary analysis comparing baseline data from two cohorts of young South Africans ages 18-24 from the Cape Town and East London metro areas who tested positive for HIV at clinics (or mobile clinics) either before or during the COVID-19 pandemic. Baseline sociodemographic, economic, and psychological outcomes were analyzed through a series of bivariate logistic regression and mediation analyses. All data were analyzed in 2023 and 2024. RESULTS: Reported food anxiety, insufficient food quality, and insufficient food quantity were lower in the cohort recruited during the COVID-19 pandemic than those recruited before the pandemic (p < 0.001). Higher levels of food insecurity predicted higher depressive and anxiety symptoms and lower self-esteem. Food anxiety, insufficient food quality, and insufficient food quantity, but not unemployment, mediated the relationship between study cohort and depressive symptoms, anxiety symptoms, and self-esteem. CONCLUSION: Food insecurity may have decreased amongst our sample of young people during the COVID-19 pandemic. Our findings build on our understanding of how the psychological well-being of young people living with HIV was affected by the COVID-19 pandemic and may lend support to interventions targeting food insecurity to improve psychological well-being in this population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".