A guide to anticipatory guidance for breastfeeding‐related pain: A concept analysis
Bibliographic record
Abstract
AIM: This article provides a concept analysis of anticipatory guidance regarding breastfeeding-related pain and establishes a definition to inform the concept's practical use. BACKGROUND: Breastfeeding-related pain is a barrier to achieving optimal breastfeeding outcomes, which can impede maternal and infant health. Education through anticipatory guidance that addresses breastfeeding-related pain can improve breastfeeding outcomes, but no formal definition is currently available for practitioner use. DESIGN: Walker and Avant's protocol for concept analyses was employed. DATA SOURCE: A comprehensive literature review was conducted using CINAHL, PubMed, Scopus, and OMNI. Search terms included anticipatory guidance, breastfeeding, pediatrics, nursing, and lactating. This identified 379 articles, of which six addressed anticipatory guidance for breastfeeding-related pain. REVIEW METHODS: Inclusion criteria asked that literature be available in English and published between 2000 and 2020. RESULTS: Three key characteristics of anticipatory guidance regarding breastfeeding-related pain were identified: timing, content, and intention. Antecedents included maternal intention to breastfeed and interaction with a healthcare provider. Consequences included improved breastfeeding outcomes, enhanced maternal understanding of challenges, and maternal empowerment in relation to pain management. CONCLUSIONS: Anticipatory guidance about breastfeeding-related pain can empower women to sustain breastfeeding in spite of challenges, thus prolonging the breastfeeding period, and subsequently improving infant nutrition and health outcomes.
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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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".