Promoting responsive care and early learning practices among caregivers of children 0–23 months in the Kyrgyz Republic: findings from integrating a counselling intervention with nutrition services
Bibliographic record
Abstract
OBJECTIVE: package into nutrition services after 10 months of implementation. DESIGN: We measured changes in RCEL practices through a pre- and post-intervention assessment comprising a household survey and observations. To implement the intervention, we trained health service staff and community volunteers to deliver RCEL counselling to caregivers of children 0-23 months of age through existing community and facility-level platforms. SETTING: Jalal-Abad and Batken regions in the Kyrgyz Republic. PARTICIPANTS: Caregivers of children aged 0-23 months at baseline. RESULTS: We found statistically significant increases in RCEL practices, availability of early learning opportunities in the home, decreases in parenting stress and improvements in complementary feeding practices after the intervention implementation period. CONCLUSIONS: was associated with improved responsive care practices and early learning opportunities. We also found that integration of RCEL with infant and young child feeding counselling did not disrupt nutrition service delivery or negatively affect complementary feeding outcomes, but rather suggest synergistic benefits. Given the importance of providing holistic care to support optimal early childhood development, these findings provide new evidence on how to strengthen the delivery of nurturing care services in the Kyrgyz Republic.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".