Prevalence of Exclusive Breastfeeding and Associated Factors among Mothers in Karongi District, Rwanda
Bibliographic record
Abstract
Background: Exclusive breastfeeding is crucial for an infant's growth and development. In Rwanda, 47% of rural children and 27% of urban children are stunted which could be linked to poor exclusive breastfeeding. Thus, this study was carried out to assess prevalence of exclusive breastfeeding and associated factors in Karongi district of Rwanda. Method: A cross-sectional design was used involving 261 mothers with infants of 6 to 9 months selected systematically with an interval two as they came to the health facilities. The data were collected using structured questionnaire. The factors independently associated with exclusive breastfeeding were determined using multivariable logistic regression analysis. Results: The prevalence of exclusive breastfeeding was 87.1%. Married mothers (AOR= 3.15; 95%CI = 1.07 - 9.28), protestant mothers (AOR= 0.15; 95%CI = 0.03 - 0.69), attending prenatal care (AOR= 19.87; 95%CI = 3.00 - 131.68), receiving postnatal care (AOR = 3.07; 95%CI = 1.31 - 7.21) and receiving breastfeeding counseling (AOR= 3.16; 95%CI = 1.03 - 9.69) were identified as independent factors associated with exclusively breastfeeding. Conclusion: The prevalence of exclusive breastfeeding was high but with various healthcare service associated factors. Therefore awareness and appropriate behavior change communication strategies on exclusive breastfeeding should be encouraged during prenatal and postpartum care for optimum practice.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".