Mothers’ Access to Social and Health Care Systems Support during Their Infants’ First Year during the COVID-19 Pandemic: A Qualitative Feminist Poststructural Study
Bibliographic record
Abstract
Social support and health services are crucial for mothers and families during their infants’ first year. The aim of this study was to explore the effect of self-isolation imposed by the COVID-19 pandemic on mothers’ access to social and health care systems support during their infants’ first year. We utilized a qualitative design using feminist poststructuralism and discourse analysis. Self-identifying mothers (n = 68) of infants aged 0 to 12 months during the COVID-19 pandemic in Nova Scotia, Canada completed an online qualitative survey. We identified three themes: (1) COVID-19 and the Social Construction of Isolation, (2) Feeling Forgotten and Dumped: Perpetuating the Invisibility of Mothering, and (3) Navigating and Negotiating Conflicting Information. Participants emphasized a need for support and the associated lack of support resulting from mandatory isolation during the COVID-19 pandemic. They did not see remote communication as equivalent to in-person connection. Participants described the need to navigate alone without adequate access to in-person postpartum and infant services. Participants identified conflicting information related to COVID-19 as a challenge. Social interactions and interactions with health care providers are crucial to the health and experiences of mothers and their infants during the first year after birth and must be sustained during times of isolation.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".