Sustainability of a Cash Transfer Program in Malawi: A CANCaRe Africa “Zero Abandonment” Pilot
Bibliographic record
Abstract
BACKGROUND: Sustainability-the continued delivery of an intervention's intended benefits after external donor support ends-is essential to ensure long-term impact and success. In 2019, a cash transfer program in Blantyre, Malawi, provided full transport reimbursement (mean ∼200 Euros/family), counseling, and patient tracking for caregivers of children with common and curable cancers. This reduced treatment abandonment from 19% to 7% (p < 0.001). We evaluated the program's sustainability over a 4-year period post-implementation. METHODS: The intervention was implemented from June 2019 to June 2020. We conducted a mixed-methods study to assess sustainment and sustainability. We evaluated the continuation of cash transfers and treatment abandonment rates among children (<16 years) newly diagnosed with common and curable cancers from 2022 to 2024. Exploratory stakeholder interviews identified perceived facilitators and barriers to sustainability. RESULTS: The program continued beyond 2020 with modifications-transport reimbursement was limited to the home district. A new donor assumed funding. Reduced abandonment rates were sustained: 9% (10/110) in 2022, 10% (13/127) in 2023, and 3% (2/70) in 2024 (with 40% still on treatment) (p = 0.28). Reported facilitators of sustainability included high acceptability, local ownership, demonstrated effectiveness, and availability of alternative donor support. Barriers included resource constraints, competing health priorities, and concerns about misuse of funds. CONCLUSION: Four years after initial funding ended, the cash transfer intervention remained active with sustained reductions in treatment abandonment. These findings highlight the potential for sustained impact of financial support programs in low-resource settings. Further research is needed to identify the key determinants of sustainability, informing future scale-up efforts.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".