A Scoping Review of Breastfeeding in Women with Chronic Diseases
Bibliographic record
Abstract
Background: Approximately 10–20% of mothers have a chronic disease. Studies on breastfeeding in women with chronic disease span multiple disciplines, and these have not been collated to synthesize knowledge and identify gaps. The objective of this review was to summarize published literature on breastfeeding in women with chronic disease. Methods: We conducted a scoping review of original research and systematic reviews identified in Medline, EMBASE, and CINAHL (1990–2019) and by hand searching on women with chronic diseases reporting on at least one breastfeeding-related topic. Conference abstracts, case-studies, and studies on pregnancy-induced conditions or lactation pharmacology were excluded. Content analysis and narrative synthesis were used to analyze findings. Results: We identified 128 articles that were predominantly quantitative (80.5%), conducted in Europe or North America (65.6%), analyzed sample sizes of <200 (57.0%), and published from 2010 onward (68.8%). Diabetes (42.2%), multiple sclerosis (MS; 19.5%), and epilepsy (13.3%) were the most common diseases studied. Breastfeeding was a primary focus in approximately half (53.1%) of the articles, though definitions were infrequently reported (32.8%). The most-studied topics were breastfeeding duration/exclusivity (55.7%), reasons for feeding behavior (19.1%), and knowledge and attitudes about breastfeeding (18.3%). Less studied topics (<10% of articles each) included milk expression behaviors, breastfeeding difficulties, and feeding supports. Conclusions: Existing literature focuses primarily on diabetes or MS, and breastfeeding behaviors and outcomes. Further research examining a broader range of chronic diseases, with large sample sizes, and sufficient breastfeeding measurement detail can improve our understanding of breastfeeding disparities in this population.
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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 | 0.021 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".