From effectiveness to sustainability: understanding the impact of CARE’s Community Score Card© social accountability approach in Ntcheu, Malawi
Bibliographic record
Abstract
We evaluated the sustainability of CARE's Community Score Card© (CSC) social accountability approach in Ntcheu, Malawi, approximately 2.5 years after the end of formal intervention activities. Using a cross-sectional, exploratory design, we conducted 41 focus groups with members of Community Health Advisory Groups (CHAGs) and youth groups and 19 semi-structured interviews with local and district government officials, project staff, and national stakeholders to understand how and in what form CSC activities are continuing. Focus groups and interviews were audio-recorded, transcribed and translated into English. Thematic coding was done using Dedoose software. Most groups were continuing to meet and implement the CSC, although some made modifications. CHAGs, youth and local government officials all attributed their continued implementation to the value that they saw in the process that allows marginalized groups within the community, including women and youth, a safe space for sharing their ideas and issues and the initial results this generated. However, lack of access to resources for implementation and challenges in convening and facilitating the interface meeting phase created barriers to continued sustainability. The CSC is sustainable by communities 2.5 years after the end of formal intervention activities. For future interventions, health systems and non-governmental organizations should plan for a transition phase with periodic refresher trainings and a small fund to support implementation, such as refreshments and transportation, to increase the likelihood of community-driven sustainability.
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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 | 0.041 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".