Cash Transfers for Improved Maternal and Child Health Data: A Pilot Study Supporting Health Management Information System in Malawi
Bibliographic record
Abstract
Abstract Background The health management information system (HMIS) is an integral component of a strong health care system. Despite its importance for decision-making, the quality of HMIS data remains of concern in low- and middle-income countries. To address challenges with the quality of maternal and child health (MCH) data gathered within Malawi's HMIS, we designed a pilot study consisting of performing regular cash transfers to district-level HMIS offices. We hypothesized that providing regular cash transfers to HMIS offices would empower staff to establish strategies and priorities based on local context, consequently obtaining and maintaining accurate, timely, and complete MCH data. Methods The pilot intervention was implemented in Mwanza district, while Chikwawa, Neno, and Ntchisi districts served as control sites. The intervention consisted of providing cash transfers to Mwanza's HMIS office following the submission of detailed budgets and lists of planned activities with their respective targets and outputs. In the control districts, we performed regular interviews with the HMIS officers to track the HMIS-related activities. We evaluated the intervention by comparing data quality between the post-intervention and pre-intervention periods in the intervention and control districts. Additionally, we conducted interviews with Mwanza's HMIS office staff to determine the acceptability and appropriateness of the intervention. Results Following the 10-month intervention period, we observed improvements in MCH data quality in the intervention district (Mwanza). The availability and completeness of MCH data collected in the registers increased by 22% and 18%, respectively. The consistency of MCH data between summary reports and electronic HMIS improved from 73–94%. The qualitative interviews confirmed that, despite some challenges, the intervention was well received by the participating HMIS office. Participants preferred our strategy to other conventional ways of supporting HMIS that fail to give HMIS offices the independence to make decisions. Conclusions This pilot intervention demonstrated an alternative approach to support HMIS offices in their daily efforts to improve data quality. Given the Ministry of Health (MoH)'s interest in strengthening its HMIS, our intervention provides a strategy that the MoH and local and international partners could consider to rapidly improve HMIS data with minimal oversight.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".