An exploration of differences in infant feeding practices among women with and without diabetes in pregnancy: A mixed‐methods study
Bibliographic record
Abstract
AIMS: (1) To determine the likelihood of full breastfeeding at 3 months postpartum in women with and without diabetes in pregnancy (DiP); (2) to explore the associations between diabetes management practices and infant feeding practices in those who had DiP and (3) to examine women's experiences of feeding their infants after having DiP. METHODS: The quantitative study used data from Alberta Pregnancy Outcomes and Nutrition (APrON) cohort study. Participants who had DiP (n = 62) were matched 1:3 to participants without DiP for pre-pregnancy BMI, parity, mode of delivery and pre-term birth. Infant feeding questionnaires, prospective breastfeeding diaries and medical chart data were analysed to determine likelihood of fully breastfeeding at 3 months postpartum. For the qualitative study, interviews were conducted with postpartum women who had DiP to explore the experiences of infant feeding. Interviews were thematically analysed, and the results were compared between women who were categorized as 'full breast feeders' or 'mixed feeders'. RESULTS: The odds of fully breastfeeding were 50% lower in women with DiP than women without DiP (OR: 0.50, 95% CI 0.25-0.99, p = 0.04). Qualitative interviews identified that although all women showed resilience in the face of infant feeding challenges, those who were fully breastfeeding reported seeking out external infant feeding supports, for example, classes or Doula's. Mixed Feeders perceived there was a lack of infant feeding information and support given to them prior to giving birth. CONCLUSION: Women with DiP may require additional prenatal and postnatal infant feeding support to be better prepared to overcome feeding challenges they may face.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".