The effectiveness on health education to prevent nipple trauma from breastfeeding: a systematic review
Bibliographic record
Abstract
Abstract Objectives: to analyze the effectiveness on health education to prevent nipple trauma in breastfeeding compared to other interventions. Methods: systematic literature review was carried out in January 2019, according to PRISMA recommendations. The searches were conducted in Cinahl, PubMed, Web of Science, Scopus, and in the references cited in the selected articles. The studies were assessed for quality and level of evidence according to the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE). Results: twelve studies were selected, all conducted at the maternities. The interventions were classified in two categories: health education on breastfeeding and the use of nipple covers. Health education on breastfeeding presents a moderate level of evidence on the prevention of nipple trauma. There is a moderate to high level of evidence on the positive effects of guaiazulene, gel or peppermint water applied in the postpartum period to prevent nipple trauma. Conclusion: health education on breastfeeding with clinical demonstration is an important strategy to prevent nipple trauma and requires more than one educational approach. The use of nipple covers requires some consideration, since they must be removed prior to breastfeeding. All interventions were conducted in the postpartum period, which points out for the necessity of clinical research to prevent nipple trauma in prenatal 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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".