The impact of a counselling intervention on nutrition practices among caregivers of children under two in the Kyrgyz Republic
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of a counselling programme to strengthen the health and nutrition behaviours of caregivers of children under 2 and the sustainability of that impact through reduced intervention intensity one year later. DESIGN: The programme trained community- and facility-based health staff to provide nutrition counselling. We conducted an impact evaluation with a modified stepped-wedge design using difference-in-differences analysis to compare indicator changes in an intervention group to a comparison group (midterm survey) and then a full intervention group to a light intervention group (final survey). SETTING: Batken and Jalal-Abad oblasts, the Kyrgyz Republic, 2020-2023. PARTICIPANTS: Caregivers of children under 2 provided 6253 responses in three telephone surveys. RESULTS: We observed statistically significant differences between the intervention and comparison groups at midterm for the percentage of children consuming vitamin A-rich foods; an increase in the intervention group (58-62 %) and a decrease in the comparison group (61-57 %). We observed similar results with exclusive breastfeeding (51-55 % in the intervention group and 48-40 % in the comparison group). There were also positive differences in other health and nutrition indicators. With the final survey results, in general, we observed statistically significant differences indicating a bigger change in full intervention areas compared to light intervention areas. We observed small negative changes in many indicators in light intervention areas. CONCLUSIONS: This evaluation highlights the importance of continued support for local interventions, particularly counselling programmes, to foster optimal nutrition behaviours.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".