Breastfeeding in the pandemic: A qualitative analysis of breastfeeding experiences among mothers from Canada and the United Kingdom
Bibliographic record
Abstract
BACKGROUND: Previous research shows that the COVID-19 pandemic resulted in both barriers and facilitators to breastfeeding. However, little research has looked specifically at first-time mothers' experiences of breastfeeding during the pandemic or compared experiences of mothers living in different countries. AIM: This research explores mothers' breastfeeding experiences to describe how the COVID-19 pandemic has affected breastfeeding journeys in Canada and the United Kingdom. METHODS: Ten semi-structured online interviews were undertaken with first-time mothers who breastfed their baby at least once during the COVID-19 pandemic and are living in Canada or the United Kingdom. Interview transcripts were coded inductively using thematic analysis. FINDINGS: One overarching theme (all on mother) and four sub-themes were identified: 1) accessing and advocating for health care, 2) social support, 3) becoming a mother in isolation, and 4) breastfeeding baby. Similar themes were constructed for both countries. DISCUSSION: Mothers reported that diminished health care and social support created challenges in their breastfeeding journey. Many mothers reported receiving virtual breastfeeding support, which was largely experienced as unhelpful. Some mothers reported fewer distractions from visitors and more one-on-one time with their infant, which helped them to establish breastfeeding and a strong mother-infant bond. CONCLUSION: In both Canada and the United Kingdom, new mothers need consistent, reliable health care and social support when breastfeeding. This study supports the need to protect breastfeeding support in the midst of a global emergency and beyond to ensure positive breastfeeding experiences for both mother and baby.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".