An Exploration of the Maternal Experiences of Breast Engorgement and Milk Leakage after Perinatal Loss
Bibliographic record
Abstract
INTRODUCTION & PURPOSE: Perinatal loss is one of the toughest events of life. Physiological milk secretion after perinatal loss adds to complicacy of the hardships of the event. The present study is aimed at exploring women's experience with breast problems and milk leakage after perinatal loss. METHODS: The Study was carried out through explorative quality approach with 18 participants. Sampling method was purposeful and selecting the participants from widest variety was ensured. Data gathering was through deep semi-structured interview and data analyses were done by conventional content analysis. Reliability and validity of the data were ensured by collecting data from a wide range of participants and frequent revisions. FINDINGS: Data analysis indicated four themes including beyond pain, longing being mother, insufficiency of provided information and coping Strategies, and beliefs and values regarding milk leakage and breast engorgement. CONCLUSION: The findings suggested that health care givers needed to inform the patients about probability milk leakage and breast engorgement and remedies to reduce pains and problems of breast engorgement.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".