Breastfeeding Support Provided by Lactation Consultants
Bibliographic record
Abstract
Importance: Breast milk offers numerous health benefits, yet breastfeeding recommendations are met less than half of the time in high-income countries. Objective: To evaluate the effect of lactation consultant (LC) interventions on breastfeeding, maternal breastfeeding self-efficacy, and infant growth compared to usual care. Data Sources: The Cochrane Central Register of Controlled Trials, MEDLINE, Embase, CINAHL, Scopus, Web of Science, and the gray literature were searched for articles published between January 1985 and July 2024. The search took place on July 10, 2024, and data analysis was performed from July to August 2024. Study Selection: Randomized clinical trials of LC interventions in high-income countries published in any language were eligible for inclusion. Data Extraction and Synthesis: Data extracted included study design, participant and intervention characteristics, and outcome data. To account for studies that reported outcomes at multiple time points, effect estimates were pooled with 3-level correlated and hierarchical effects models. Meta-regression was performed for clinically important characteristics, such as the time point when the outcome was measured, intervention intensity, and participant income. Main Outcomes and Measures: The primary outcome was stopping exclusive breastfeeding. Secondary outcomes included stopping any breastfeeding, exclusive breastfeeding and any breastfeeding duration, maternal breastfeeding self-efficacy, infant overweight and obesity, and infant growth. Results: The search yielded 6476 records, of which 40 studies were included involving 8582 participants. Studies were published between 1992 and 2024, and most studies (n = 22) were conducted in the US. Compared to usual care, LC interventions reduced the risk of stopping exclusive breastfeeding (risk ratio [RR], 0.96; 95% CI, 0.94-0.99) and any breastfeeding (RR, 0.92; 95% CI, 0.87-0.96) and increased any breastfeeding duration by 3.63 weeks (95% CI, 0.13-7.12). There was weak evidence that LC interventions increased exclusive breastfeeding duration (mean difference [MD], 1.44 weeks; 95% CI, -2.73 to 5.60), maternal breastfeeding self-efficacy (MD, 2.83; 95% CI, -1.23 to 6.90), or the risk of infant overweight and obesity (RR, 1.52; 95% CI, 0.94-2.46). Meta-regression showed that LC interventions were more effective at reducing the risk for stopping exclusive breastfeeding (P = .01) and any breastfeeding (P < .001) the earlier that breastfeeding was measured in the postpartum period. LC interventions with a higher intensity (ie, number of LC visits) were more effective at reducing the risk for stopping any breastfeeding (P = .04). Conclusions and Relevance: According to the results of this systematic review and meta-analysis, LC interventions are a promising intervention for improving exclusive breastfeeding and any breastfeeding in high-income countries.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".