Promoting responsive care and early learning practices in Northern Ghana: results from a counselling intervention within nutrition and health services
Bibliographic record
Abstract
OBJECTIVE: This study assesses change in caregiver practices after integrating responsive care and early learning (RCEL) in nutrition and health services and community platforms in northern Ghana. DESIGN: We trained health facility workers and community health volunteers to deliver RCEL counselling to caregivers of children under 2 years of age through existing health facilities and community groups. We assessed changes in caregivers' RCEL practices before and after the intervention with a household questionnaire and caregiver-child observations. SETTING: The study took place in Sagnarigu, Gushegu, Wa East and Mamprugu-Moagduri districts from April 2022 to March 2023. Study sites included seventy-nine child welfare clinics (CWC) at Ghana Health Service facilities and eighty village savings and loan association (VSLA) groups. PARTICIPANTS: We enrolled 211 adult caregivers in the study sites who had children 0-23 months at baseline and were enrolled in a CWC or a VSLA. RESULTS: We observed improvements in RCEL and infant and young child feeding practices, opportunities for early learning (e.g. access to books and playthings) in the home environment and reductions in parental stress. CONCLUSIONS: This study demonstrates the effectiveness of integrating RCEL content into existing nutrition and health services. The findings can be used to develop, enhance and advocate for policies integrating RCEL into existing services and platforms in Ghana. Future research may explore the relationship between positive changes in caregiver behaviour and improvements in child development outcomes as well as strategies for enhancing paternal engagement in care practices, improving child supervision and ensuring an enabling environment.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".