Unveiling breastfeeding knowledge among Muslim women in Qatar: an exploratory study
Bibliographic record
Abstract
Low global breastfeeding rates pose a significant public health issue. In Qatar, the majority of Muslim women do not breastfeed their babies despite guidelines from Islamic and health organizations. Various factors influence women’s decisions to breastfeed, but limited research exists on breastfeeding knowledge and its predictors among Muslim women. This study aims to (a) assess breastfeeding knowledge and its sources among Muslim women in Qatar and (b) identify predictors of breastfeeding knowledge. A group of 414 postpartum Muslim women was recruited using convenience sampling. Data on breastfeeding knowledge was collected using a pre-developed questionnaire, summarized, and overall knowledge scores were computed. Predictors of breastfeeding knowledge were identified using linear regression. Although participants demonstrated accurate breastfeeding knowledge, notable gaps remain, particularly in understanding the protective effects of breastfeeding against conditions such as diabetes (48.1%) and ovarian cancers (52.7%), as well as guidelines on breastfeeding initiation (25.0%) and exclusivity (29.7%). The participants identified television and social media, mothers, and mothers-in-law as the primary sources of their breastfeeding knowledge. Predictors of higher breastfeeding knowledge included higher levels of education, living with a husband only, prior breastfeeding experience, and the intention to exclusively breastfeed. To improve breastfeeding knowledge and practices, targeted educational programs and family-oriented media campaigns that address these knowledge gaps are essential. Additionally, providing accessible counseling support can reinforce accurate breastfeeding information and create supportive breastfeeding environments. These strategies are crucial for bridging knowledge gaps and promoting breastfeeding rates in Qatar and globally.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".