The rates and factors of perceived insufficient milk supply: A systematic review
Bibliographic record
Abstract
Perceived insufficient milk supply (PIMS) is one of the major reasons for discontinued breastfeeding. We aimed to estimate the rates and evaluate related factors of PIMS. We searched four databases for relevant articles published from January 2000 to March 2021. We then performed a meta-analysis of the pooled data to estimate the rates and related factors of PIMS using Stata 15.0. Descriptive analyses of textual data were performed to summarise the related factors of PIMS if data could not be synthesised quantitatively. The quality of included studies was assessed using Newcastle-Ottawa scale (NOS), AHRQ checklist or Consolidated Criteria for Reporting Qualitative Research (COREQ). Overall, 27 studies were included in this review. At different periods after delivery, approximately 50% of mothers reported PIMS as the reason for stopping breastfeeding, while for breastfeeding mothers, the incidence of PIMS ranged from 10% to 25%. Breastfeeding initiation (OR 4.22, 95%CI 1.57-11.34) and breastfeeding knowledge (OR 7.10, 95%CI 2.00-25.26) were two factors influencing PIMS. Besides, PIMS had a strong negative relationship with breastfeeding self-efficacy (r = -0.57); moderate negative association with infant suck ability (r = -0.46) and planned breastfeeding duration (r = -0.45); and a moderate positive correlation with formula supplementation (r = 0.42). Descriptive analyses revealed that infant crying was reported to be a sign of PIMS, and inadequate intake of energy/liquids was a reported cause of it. This review identified a high proportion of women reporting PIMS, particularly among those who stopped breastfeeding. Deliberate interventions were needed to improve breastfeeding for mothers at risk.
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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 | 0.022 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".