The Neo-MILK Web App as a Health Technology to Support Mothers of Preterm and Sick Neonates During Lactation: Usability Study
Bibliographic record
Abstract
Background: Mothers of sick and preterm infants need support to establish and maintain lactation. Although many health technologies on breastfeeding are available, most lack in evidence-based information and are therefore not appropriate for educating mothers. Furthermore, they do not focus on the special challenges of mother-infant separation during lactation in mothers of sick or preterm infants. Objective: The aim of this study is to examine the usability and perceived usefulness of the evidence-based information about lactation and documentation tools contained in the Neo-MILK web app. Methods: A cross-sectional online survey was conducted among mothers of sick and preterm infants admitted to a neonatal intensive care unit in Germany. Descriptive statistics were calculated for the System Usability Scale (SUS) and for self-developed items pertaining to overall satisfaction and perceived usefulness of the app. These included items on evidence-based information and the usability of tracking functions. Results: Of 341 mothers who were contacted, 80 responded (response rate, 23.4%), and data from 63 mothers were analyzed. The mean SUS score was 76.4. The overall satisfaction rate was high, with 84% (n=53) of respondents indicating that they were either satisfied or very satisfied. Further, 82% (n=52) were inclined to recommend the web app to other parents. On average, the evidence-based information was perceived as helpful, more detailed, and not contradictory compared to information provided at the hospital. At the same time, most of the users reported that the Neo-MILK web app did not exert pressure to provide breast milk to their infants. Approximately 71% (n=45) of the mothers used the documentation tool in the web app several times per week to track their milk volumes. Conclusions: By combining evidence-based information and useful tools to document milk volume, the Neo-MILK web app was high rated in usability and perceived usefulness. Considering the limitations of the study, this web app appears to be a valuable tool for educating and supporting pump-dependent mothers of sick and preterm infants during lactation.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".