Evaluating Programmatic Effectiveness and Implementation: An Assessment of Factors Affecting Change in the Practice of Exclusive Breastfeeding in Ethiopia and Mali
Bibliographic record
Abstract
Exclusive breastfeeding (EBF) to six months of age is one of the most effective interventions to ensure the survival of newborns and children, yet programmatic activities implemented to increase EBF rates are only variably successful. With funding from the Canadian government's Muskoka Initiative Partnership Program, two NGOs (CARE and Save the Children) implemented programs in Ethiopia and Mali to improve maternal, newborn, and child health outcomes, including EBF. Programmatic activities were diverse, including the development of educational material, community‐based counselling, radio messaging, and mother‐to‐mother support groups. This study aimed to quantitatively assess the observed change in EBF coverage from cross‐sectional survey data, and qualitatively determine contextual factors that influenced the implementation of EBF programmes from focus group discussions (n=15; included mothers and community leaders) and key informant interviews (n=17; included government officials and NGO personnel). Baseline and endline data showed a net increase in the EBF rate in both countries (Mali: 27.3% [95% CI: 19.2 to 35.3] to 66.3% [95% CI: 57.0 to 75.7], P <0.05; Ethiopia: 69.4% [95% CI: 62.4 to 76.3] to 75.1% [95% CI: 69.9 to 80.3], not significant). When analyzed inductively (themes identified from the qualitative data) and deductively (categories assigned to the qualitative data from an existing framework), some of the most influential factors that emerged for EBF adoption were engaging influential community members as ‘champions’ for change; repeated exposure to information about EBF practice and benefits; exposure to community members’ EBF testimonials; and community recognition of a strong need for change to improve infant health outcomes. Traditional beliefs, knowledge, and practices regarding infant feeding and gender role‐related expectations were also found to be important factors for EBF behavioural change. Ultimately, the identified factors for successful implementation and uptake of EBF activities will help to guide and refine existing implementation frameworks, as well as programmatic approaches to increase the number of mothers who practice EBF. Support or Funding Information Financial support was provided by the Government of Canada through Foreign Affairs, Trade, and Development Canada (DFATD).
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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 | 0.055 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".