Unconditional cash transfers for clinical and economic outcomes among HIV-affected Ugandan households
Bibliographic record
Abstract
BACKGROUND: HIV infection has profound clinical and economic costs at the household level. This is particularly important in low-income settings, where access to additional sources of income or loans may be limited. While several microfinance interventions have been proposed, unconditional cash grants, a strategy to allow participants to choose how to use finances that may improve household security and health, has not previously been evaluated. METHODS: We examined the effect of an unconditional cash transfer to HIV-infected individuals using a 2 × 2 factorial randomized trial in two rural districts in Uganda. Our primary outcomes were changes in CD4 cell count, sexual behaviors, and adherence to ART. Secondary outcomes were changes in household food security and adult mental health. We applied a Bayesian approach for our primary analysis. RESULTS: We randomized 2170 patients as participants, with 1081 receiving a cash grant. We found no important intervention effects on CD4 T-cell counts between groups [mean difference 35.48, 95% credible interval (CrI) -59.9 to 1131.6], food security [odds ratio (OR) 1.22, 95% CrI: 0.47 to 3.02], medication adherence (OR 3.15, 95% CrI: 0.58 to 18.15), or sexual behavior (OR 0.45 95% CrI: 0.12 to 1.55), or health expenditure in the previous 3 weeks (mean difference $2.65, 95% CrI: -9.30 to 15.69). In secondary analysis, we detected an effect of mental planning on CD4 cell count change between groups (104.2 cells, 9% CrI: 5.99 to 202.16). We did not have data on viral load outcomes. CONCLUSION: Although all outcomes were associated with favorable point estimates, our trial did not demonstrate important effects of unconditional cash grants on health outcomes of HIV-positive patients receiving treatment.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".