Updating Clinical Practices to Promote and Protect Human Milk and Breastfeeding in a COVID-19 Era
Bibliographic record
Abstract
The COVID-19 pandemic has impacted breastfeeding and lactation globally, with clinical practices implemented early in the pandemic being mostly anti-breastfeeding, e.g., separation of mothers from their infants, and not evidence based. As the pandemic has progressed, evidence has emerged reconfirming the value of human milk and the importance of protecting and supporting breastfeeding, especially the initiation of lactation. However, it is clear that COVID-19 has changed the clinical care paradigm around breastfeeding and lactation support and, as such, it is imperative that practices adapt and evolve to maintain the emphasis on lactation support. We participated in a round table conference aiming to rescue and develop protocols and practices that support breastfeeding during the COVID-19 pandemic. One key area to target will be to maximize the use of the antenatal period. The early identification of lactation risk factors together with the development of person-centered methods to deliver breastfeeding information and education to parents-to-be will be critical. In addition, the establishment of a hospital culture that values breastfeeding and prioritizes the use of human milk will be integral for the motivation of health care professionals. That culture will also support active management of the initiation of lactation and the development of a 'back-up plan' toolkit to support the mother experiencing lactation difficulties. Post-discharge support will also be crucial with the development of both in-person and virtual lactation support programs, in particular for the immediate post-discharge period to benefit mothers who experience an early discharge process. These measures will allow for a new, adapted framework of practice that acknowledges the current COVID-19 paradigm and maintains the emphasis on the need to protect and support breastfeeding and the use of human milk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".