First-time Mothers' Experiences of Breastfeeding Support During the COVID-19 Pandemic: An Interpretive Description Study
Bibliographic record
Abstract
During the COVID-19 pandemic, it was difficult for mothers in Ontario to obtain the breastfeeding support they required due to pressure on the healthcare system, social restrictions, and redeployment of healthcare professionals from perinatal services to the pandemic response (Canadian Institute for Health Information [CIHI], 2022; Jack et al., 2021; Rudrum, 2021). The purpose of this interpretive description study was to better understand the impact of the COVID-19 pandemic upon first-time mothers' experiences and perceptions of breastfeeding support in Ontario, Canada. Eligible participants were recruited using purposeful and snowball sampling. Thirteen one-on-one, semi-structured interviews were conducted using a video-conferencing software. One over-arching theme, on their own, and three major themes were identified by the researchers. The first theme, lack of support, is broken down into subthemes lack of practical support, lack of informational support, lack of social support and lack of emotional and esteem-building support. The second theme, figuring it out, is further categorized into the subthemes understanding, taking risks, and motivation and resourcefulness. The third theme, emotional hardships, is broken down into two sub-themes, isolation and it was difficult. The findings from this study have implications for nursing practice, policy, and research, that support the need for more effective pandemic preparedness from the province, including, consistent access to formal and informal breastfeeding support services.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".