Attitudes of Saudi Mothers Towards Breastfeeding: A Cross–Sectional Survey in Taif Region, Saudi Arabia
Bibliographic record
Abstract
Background: Mothers’ attitudes are strong predicators of choice of infant feeding method. This study was conducted to measure attitudes towards breastfeeding among Saudi mothers. Methods: A cross-sectional survey was conducted during April 2013 among Saudi nursing mothers in Taif Region; Kingdom of Saudi Arabia (KSA). Data was collected by trained pharmacy female students through face-to-face interview method using structured questionnaire. Mothers’ attitudes towards breastfeeding were assessed by The Iowa Infant Feeding Attitude Scale (IIFAS). Results: A total of 387 mothers were included of them 204 (52.7%) aged < 32 years and 334 (86.3%) were residents in the town. University or college graduates were 262 (67.7%). Overall 181 (46.8%) of the mothers had positive attitudes towards breastfeeding, while 206 (53.2%) held negative ones. Correlating mothers’ total attitudes towards breastfeeding to their demographics showed that; out of the mothers aged < 32 years 118 (57.8%) had significantly more positive attitudes than older ones (aged > 32 years) 63 (34.4%), (P < 0.001). Cross tabulation of the method of infant feeding and mothers’ total attitudes towards breastfeeding showed that mothers used formula feeding or mixed feeding method 120 (49.0%) had significantly positive attitudes towards breastfeeding more than breastfeeding ones 61(43.0%), (P =0.025). Conclusion: The rate of exclusive breastfeeding was low. Positive attitudes towards breastfeeding was found to be more among mothers used formula or mixed feeding method. Educational interventions are needed to raise awareness and upgrade mothers’ knowledge on infant breastfeeding.
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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 | 0.000 | 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".