Impact of Hospital Breast Feeding Awareness Among Lactating Mothers in Maternal and Children Hospital, AL-Hassa, Kingdom of Saudi Arabia
Bibliographic record
Abstract
BACKGROUND & AIM: Insufficient knowledge and practice of breastfeeding may have serious disadvantages both on mother and child health. This study explores methods used in MCH based breastfeeding awareness program, level of benefit gained by newly delivered mothers after receiving the awareness, and the impact of mother’s sociodemographic on the level of perceived benefit gained by them. SUBJECTS & METHODS: A prospective cross-sectional study applying random sampling technique was established. A self-administered questionnaire was distributed targeting newly delivered mother in maternal and child hospital in Al-Hassa, Saudi Arabia. It included two main parts: socio-demographic characteristics of the mothers, and questions related to the hospital breastfeeding awareness program. RESULTS: from the overall sample, hospital awareness was received by 47.5% of newly delivered mothers. The most common method to provide the knowledge was the Verbal demonstration representing 50% of the mothers. They were followed by brochures representing 39% and last, audios constituting only 3.6%. From these methods, the verbal demonstration showed to be the one with the highest level of satisfaction and benefit reaching up to 85%. CONCLUSION: Breastfeeding awareness has a significant impact among both newly and non-newly delivered mothers with Hospitals playing a major role in this health education. The choice of method to provide breastfeeding awareness can contribute to the compliance of mothers as well as the level of their benefit and satisfaction.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".