Impact of Cash for Health Assistance on Healthcare Access and Health-Seeking Behaviors for Families of Pregnant Women in Sindh, Pakistan
Bibliographic record
Abstract
Background: The 2022 Pakistan floods devastated healthcare access for pregnant women in already impoverished areas in Sindh province. This study examines how Cash for Health assistance (CH) of USD 112 alleviated financial burdens and improved maternal health outcomes and resilience, bridging a critical literature gap on cash effectiveness in humanitarian crises. Methodology: This study used a mixed-methods approach to assess the CH assistance intervention for families of pregnant/lactating women in flood-affected rural Sindh, Pakistan. A pre-post quantitative analysis of baseline (May–June 2024) and endline (August–November 2024) survey data in ~100 villages (Jamshoro/Sehwan) examined changes in healthcare access, expenditure, and preferences using t-tests, proportion tests, and multivariable regression. Concurrently, five qualitative case studies from key informant interviews provided thematic content analysis, triangulating findings on economic, health, and social impacts. Results: Respondents predominantly had low literacy rates and were from households of daily wage laborers in vulnerable, flood-affected areas. While income and education remained low, instances of forgone care due to financial barriers increased (68% to 97%, p < 0.001). CH significantly improved healthcare access (58% to 98%, p < 0.001). Access to regular physicians (20% to 69%) and private facilities (10% to 41%) notably expanded. Healthcare expenditure significantly increased from USD 9.3 to USD 25, with a shift in spending preference towards medication, consultations, and diagnostics. CH also significantly improved food security (21% to 97%), meal frequency, and overall household stability, including reducing domestic violence. Qualitative data emphasized pre-existing vulnerabilities and CH’s role in addressing health, nutrition, and psychosocial needs. Conclusions: CH significantly improved healthcare access and reduced financial burdens for vulnerable pregnant women post-disaster. However, a sustainable impact requires integrated “cash plus” models, combining financial aid with stronger health systems, psychosocial support, and literacy for long-term resilience.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".