Mothers, Household Bubbles, and Social Support During the First Wave of the COVID-19 Pandemic
Bibliographic record
Abstract
Parents of young children experienced many stressors due to stay-at-home directives in the first wave of the COVID-19 pandemic. Bubbles were implemented by some governments, allowing households to connect with another household while minimizing contagion risk, but little is known about their effectiveness. We explored the social support experiences of Canadian mothers living in Nova Scotia during this first wave, focusing on whether they perceived household bubbles to be helpful in reducing parenting stress. In-depth interviews were completed with 18 mothers (aged 21–49) who had at least one child under the age of 12 during the first shutdown. Interviews focused on how they coped during the initial shutdown period, the immediate time after they paired up with another household, and what was happening for them currently (approximately eight to ten months later). Data were analyzed using qualitative description and content analysis through application of topic, descriptive, and analytical coding; memo writing; and matrix analysis. Deciding who to bubble with typically focused on direct support for parents or having playmates for children. Having a bubble arrangement reduced the pressure of the situation, and perceptions of future emergency backup support also reduced anxiety levels. Support from family members who lived far away, however, was still key for some families. Household bubbles play an important role in reducing stress levels during a pandemic through received and perceived support. As the COVID-19 pandemic evolves, policy directives and support interventions need to enhance social support for parents and peer interactions for young children.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".