Grandparents’ and great-grandparents’ knowledge, attitude and experiences supporting breastfeeding: A non-experimental study
Bibliographic record
Abstract
Background: Breastfeeding has many health benefits for babies and mothers, but many families face challenges in meeting breastfeeding recommendations. Grandparents often influence new parents’ infant feeding experiences, yet little is known about their knowledge, attitudes, experiences, and needs regarding supporting breastfeeding in Canada. Purpose: This non-experimental exploratory descriptive study aimed to explore the knowledge, attitudes, and experiences of grandparents and great-grandparents in supporting breastfeeding to inform intergenerational breastfeeding promotion programs. Methods: An online survey was completed by 111 grandparents and great-grandparents living in Canada. Results: Approximately, 96% of participants had one or more children that were breastfed and 53% had a child breastfeed for over 24 months. The mean knowledge score was 78, (S.D 5.5) and the mean attitude score was 69.7 (S.D 8.71). These scores were significantly positively correlated and higher among participants who had received breastfeeding education (p=0.01). Conclusion: Grandparents and great-grandparents want information tailored to their role in supporting breastfeeding. Providing them with accessible, trustworthy, and respectful education tailored to their experiences would assist with the provision of inclusive intergenerational family-centered care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".