The Effectiveness of Virtual Lactation Support: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The World Health Organization recommends lactation support to enhance the rates of exclusive breastfeeding. Access to in person lactation support may be limited due to scarcity of resources (e.g., healthcare professionals) and geography. Advances in technology have allowed lactation supports to be offered virtually through information and communication technologies (i.e., telephone, internet, and social media). RESEARCH AIMS: To (1) critically review and (2) statistically analyze the effectiveness of virtual lactation support for postpartum mothers' exclusive breastfeeding for up to 6 months. METHODS: A systematic review and meta-analysis were conducted using PRISMA guidelines. Studies were included if they were (a) randomized controlled trials, (b) with a virtual lactation support intervention during the postpartum period, (c) reported on exclusive breastfeeding outcomes. Two reviewers independently assessed the risk of bias and extracted data. The prevalence of exclusive breastfeeding in each group and the total number of participants randomized for each group were entered into random-effects meta-analyses to calculate a pooled relative risk (RR) at three different time points (1, 4, and 6 months). The sample size was 19 randomized control trials. RESULTS: < .001). CONCLUSION: In this meta-analysis of randomized controlled trials comparing virtual lactation support with other postnatal maternity care, virtual lactation support was associated with increasing exclusive breastfeeding rates at 1 month and 6 months postpartum.The study protocol was registered (CRD42021256433) with PROSPERO.
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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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".