Perceived insufficient milk among primiparous, fully breastfeeding women: Is infant crying important?
Bibliographic record
Abstract
Breastfeeding mothers often report perceived insufficient milk (PIM) believing their infant is crying too much, which leads to introducing formula and the early abandonment of breastfeeding. We sought to determine if infant crying was associated with reported PIM (yes/no) and number of problems associated with lactation (lactation problem score [LPS] 6-point Likert scale) before formula introduction. Primiparous breastfeeding mothers were recruited at birth and visited at 1, 2 and 4 weeks. Among those fully breastfeeding at 1 week (N = 230), infant crying variables based on maternal reports were not associated with PIM at 1 week, but LPS was. However, a mother's expectation that her infant would cry more than other infants was associated with increased odds of reporting PIM at 2 and 4 weeks, as were delayed onset of lactation and previous LPS. At 1 week, crying variables (frequency, difficulty in soothing) were associated with LPS along with percent weight change. Delayed onset of lactation, infant care style, number of breastfeeds and previous LPS were longitudinally associated with change in LPS from 1 to 2 weeks and 2 to 4 weeks. Our data suggest that reported infant crying is associated with PIM and LPS in the first 4 weeks of life. Guidance on what to expect in crying behaviour and the impact of infant care style may be beneficial in reducing PIM and LPS in the first month.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".