Shamba Maisha: A pilot study assessing impacts of a micro-irrigation intervention on the health and economic wellbeing of HIV patients
Bibliographic record
Abstract
BACKGROUND: HIV/AIDS negatively impacts poverty alleviation and food security, which reciprocally hinder the rapid scale up and effectiveness of HIV care programs. Nyanza province has the highest HIV prevalence (15.3%), and is the third highest contributor (2.4 million people) to rural poverty in Kenya. Thus, we tested the feasibility of providing a micro-irrigation pump to HIV-positive farmers in order to evaluate its impact on health and economic advancement among HIV-positive patients and their families. METHODS: Thirty HIV-positive patients enrolled in the Family AIDS Care and Education Services (FACES) program in Kisumu, Kenya were provided a micro-financed loan to receive an irrigation pump and farming guidance from KickStart, the developer of the pump. Economic data, CD4 counts, household health and loan repayment history were collected 12 months after the pumps were distributed. RESULTS: Mean annual family income increased by $1,332 over baseline. CD4 counts did not change significantly. Though income increased, only three (10%) participants had paid off more than a quarter of the loan. CONCLUSIONS: We demonstrated the feasibility of an income-generating micro-irrigation intervention among HIV-positive patients and the collection of health and economic data. While family income improved significantly, loan repayment rates were low- likely complicated by the drought that occurred in Kenya during the intervention period.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".