A Quality Improvement Initiative to Increase the Milk Donation to the Human Milk Bank Post-Coronavirus Disease-19 Pandemic
Bibliographic record
Abstract
Background and Objective: Donor human milk (DHM) from the human milk bank (HMB) is the next best alterative in circumstances when mother's own milk is not available. There was a steep decline in the volume of DHM collected during the coronavirus disease-19 (COVID-19) pandemic due to various factors, while DHM demand increased. Hence, a quality improvement (QI) study was conducted to increase the volume of milk donation to HMB from postpandemic baseline of 300–400 to 1,000 mL/day over 8 weeks. Materials and Methods:Fish bone analysis was used to identify the potential barriers, and four Plan-Do-Study-Act (PDSA) cycles were conducted from January 2021 to March 2021 to address the key barriers. In the first PDSA cycle, training of health care providers was done. Sessions for educating mothers in the second PDSA cycle and individualized one-to-one counseling of mothers by a mother support group were done in the third PDSA cycle. The availability of breast pump was increased in the fourth PDSA cycle. Sustainability of the interventions was studied for 6 months and data were analyzed. Results:The average DHM collected per day at the end of each PDSA cycle was 900, 1,500, 1,000, and 1,100 mL. Although the sustenance phase was affected by the second COVID-19 wave, prompt identification of the issues and timely interventions prevented the donated volume from dropping to preintervention levels. Conclusion:QI initiatives customized for local settings can result in significant improvement in voluntary milk donation in HMB, which can result in more availability of DHM to premature babies.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".