Perceptions on donated human milk and human milk banking in Nairobi, Kenya
Bibliographic record
Abstract
Donor human milk (DHM) is recomended as the best alternative when use of mothers' own milk is not a feasible option. Kenya has not yet established human milk banks (HMBs) for provision of safe DHM, which is free from any physical, chemical, microbiological contaminants or pathogens. This study aimed to establish the perceptions on donating and using DHM, and establishing HMBs in Kenya. Qualitative data were collected through 17 focus group discussions, 29 key informant interviews, and 25 in-depth interviews, with women of childbearing age, community members, health workers, and policy makers. Quantitative interviews were conducted with 868 mothers of children younger than 3 years. Descriptive analysis of quantitative data was performed in STATA software, whereas qualitative interviews were coded using NVIVO and analysed thematically. Majority of them had a positive attitude towards donating breast milk to a HMB (80%) and feeding children on DHM (87%). At a personal level, participants were more willing to donate their milk to HMBs (78%) than using DHM for their own children (59%). The main concerns on donation and use of DHM were personal dislikes, fear of transmission of diseases including HIV, and hygiene concerns. Ensuring safety of DHM was considered important in enhancing acceptability of DHM and successful establishment of the HMBs. When establishing HMBs, Kenya must take into consideration communication strategies to address the main concerns raised regarding the quality and safety of the DHM. The findings will contribute to the development of HMB guidelines in Kenya and other African contexts.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".