Paternal Participation in Optimal Infant and Young Child Feeding Practice and Associated Factors: A Community-Based Analytical Cross-Sectional Study from Chronically Food-Insecure Communities, Ethiopia
Bibliographic record
Abstract
Evidence shows that paternal participation helps to improve the utilization of optimal infant and young child feeding (IYCF) practices. However, little has been known about chronically food-insecure (CFI) communities. The aim of this study to assess paternal participation in optimal IYCF practice and associated factors in the Dodota district of the Arsi zone, Ethiopia, 2022. A community-based cross-sectional study involving 1152 fathers was conducted after ethical clearance was obtained. Measures of optimal IYCF are based on criteria established by the World Health Organization. We analyzed the data using bivariate and multivariate logistic regression models. Results indicated that paternal participation in optimal IYCF practice was 26.2%. The percentages of early initiation of breastfeeding (EIBF), exclusive breastfeeding (EBF), and minimum acceptable diet (MAD) were respectively 72.9%, 66.1%, and 37.3%. Paternal knowledge of IYCF (AOR:3.2, P <0.01), education (AOR:2.1, P<0.05), income (AOR:1.8, P<0.05), child's age (AOR 1.7, P<0.05), and the number of children (AOR:2.1, P<0.05) were the predictors of the outcome. Large-scale, in-depth studies should be required to justify the result unequivocally.
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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 | 0.001 |
| 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 | 0.000 |
| 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".