Program Impact Pathway Analysis Reveals Implementation Challenges that Limited the Incentive Value of Conditional Cash Transfers Aimed at Improving Maternal and Child Health Care Use in Mali
Bibliographic record
Abstract
The program “Santé Nutritionnelle à Assise Communautaire à Kayes” (SNACK) in Mali aimed to improve child linear growth through a set of interventions targeted to mothers and children during pregnancy and up to the child's second birthday. Distributions of cash to mothers and/or lipid-based nutrient supplement to children 6–23 mo of age were added to SNACK to increase attendance at community health centers (CHCs). The aim of this study, which was embedded in a cluster-randomized impact evaluation of the program, was to assess the incentive value of the cash in relation to CHC attendance. We used a mixed-methods approach. We collected quantitative data on cash receipt and CHC attendance in a midline survey of mother–child pairs (n = 3443). A program impact pathway analysis guided qualitative data collection and analysis. Twelve CHCs were purposively selected in study groups that received cash. We conducted semistructured continuous observations of cash distributions in 11 CHCs (n = 22) and semistructured qualitative interviews with frontline workers (FLWs) (n = 71) and mothers (n = 22) who were purposively selected from the midline survey. FLWs’ knowledge of the objective and implementation plan of the cash program component was limited. A challenging physical environment and insufficient cash available for each distribution were identified as causes of irregularities in cash distributions. Most mothers mentioned having to return several times to receive their cash. Child health was identified as the main motivation to attend CHCs and cash was described as an additional benefit. Implementation constraints related to remoteness and inaccessibility may have undermined the incentive value of the cash transfers in the SNACK program. Additional research is needed to identify interventions that not only incentivize mothers to participate but that can be implemented effectively and with high quality in challenging contexts such as rural areas of Mali.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".