The Effect of Post Partum Breastfeeding Peer Support on Breastfeeding Success Outcomes
Bibliographic record
Abstract
abstract: The health benefits of breastfeeding are well documented and exclusive breastfeeding for at least the first six months of life is the target of national and global health care organizations. Although initial breastfeeding is on the rise, the percentage of infants still breastfeeding at six months drops significantly. In the population of newly delivered mothers of an obstetric practice, there is no readily accessible breastfeeding support offered following hospital discharge. A review of relevant literature revealed that lack of support is often cited as a key factor in the discontinuation of breastfeeding, whereas the evidence shows that participation in peer support has a positive effect on breastfeeding self-efficacy, which can have a positive effect on breastfeeding duration. To address this problem, the initiation of a breastfeeding closed social network Facebook group for this practice setting population was developed and implemented to provide readily accessible peer support and have a positive effect on the outcome of breastfeeding self-efficacy. Three months after initiation of the Facebook group, an anonymous voluntary survey was offered to group members, and 25 members participated in the survey. Responses demonstrated that peer support is helpful with breastfeeding confidence and that, following participation in the group, the respondents wanted to continue 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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".