Rewarding behavior change in rural communities: Pathways for a sustained inclusive change for child health
Bibliographic record
Abstract
Worldwide, pneumonia, diarrhea, and malaria remain leading causes of death for children under the age of five, even though these diseases are preventable and can be treated. In public health, complex behavioral change interventions are often used. These interventions employ multi-component strategies and work on domains, such as education, policy, and environmental change, to promote prevention, control, and management of childhood diseases. In Tando Muhammad Khan (TMK), the Community Mobilization and Community Incentivization (CoMIC) trial employed a complex, participatory community engagement strategy and included conditional community-based incentives and showed promising results by improving child health related behaviors. A study was conducted to explore the experiences and perceptions of community members regarding the implementation and engagement processes of the CoMIC trial. A total of 13 IDIs and 16 FGDs were conducted to understand the factors behind the community engagement and behavior change that led to the success of the CoMIC trial and its adaptability for wider scale-up. The study identified four key motives for driving community engagement: egoism, altruism, collectivism, and principlism. The community's close-knit social structure and shared sense of collective growth played a crucial role in encouraging participation and adapting to required behaviors. The trial focused on empowering the community by reinforcing health-seeking behaviors as a community responsibility, promoting cost-sharing to ensure long-term sustainability, and creating collective ownership through active community engagement and Conditional, Collective, Community-based Incentives (C3Is). By strengthening WASH and IYCF practices, increasing immunization uptake, promoting care-seeking behaviors, the intervention aimed to reduce the burden of childhood illnesses such as diarrhea, pneumonia, and malaria, ultimately improving child health outcomes.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".