A Path Analysis to Identify Factors Influencing the Provision of Water in Addition to Breast Milk by Mothers of Infants under Six Months of Age in Conakry and Kindia Regions, Republic of Guinea
Bibliographic record
Abstract
Water provision to infants under six months of age (IU6M) can hamper exclusive breastfeeding (EBF). Understanding factors and their relationships influencing this practice is needed to tailor EBF promotion programs. Using a validated questionnaire, this study aims to identify pathways in which individual factors and the environment interact to affect the provision of water in addition to breast milk among 300 mothers of IU6M. Our finding shows that 75% of mothers intended to provide water in addition to breast milk to their IU6M and that about 60% reported doing it. Results of the final path show that the subjective norm/SN (β = 0.432, p < 0.001), the attitude (β = 0.349, p < 0.001), and to a lesser extent the perceived control/PC (β = 0.141, p = 0.005) predict the intention of mothers to provide water in addition to breast milk to their IU6M. The environment scores predict the attitude (β = 0.210, p = 0.001) and the SN (β = 0.284, p < 0.001). Having the mother practicing early breastfeeding initiation at birth positively predicted the PC score (β = 0.157, p = 0.017) and predicted an increasing score of SN (β = 0.221, p = 0.003). Even though predicting the final behavior is complex, this research provides directions to nutrition education programs to tailor their content to the context and be more efficient in reducing the proportion of women providing water to their IU6M, hence contributing to the improvement of EBF.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".