Exploring resilience and self-care among mothers in Ontario during the COVID-19 pandemic: a qualitative study
Bibliographic record
Abstract
Introduction During the pandemic governments and public health sectors promoted resilience as a strategy to cope with the socio-economic precarity introduced by COVID, including additional maternal childcare. However, the ways that resilience was operationalized in mothers’ lives is not well understood and neither is the degree to which they cared for themselves. Our study addressed this research gap and contributed novel insights to the pandemic literature about mothering, childcare, and wellness.Methods This article draws upon data from a cross- sectional project with 20 mothers living in Ontario between February and October of 2022. Specifically, it features the insights of 9 participants who talked at length about self-care during semi-structured interviews conducted over Zoom. Data were analyzed using Quirkos software alongside theoretical insights from ecological and feminist scholars.Results Three themes emerged in the women’s self-care narratives: (1) Physical activities/spatial considerations; (2) Emotional vulnerability; and (3) Intensive mothering. Our participants inhabited a spectrum of self-care that included moments of relief, self-surveillance of their mothering, and making sense of who they were while the pandemic unfolded around them.Discussion These findings highlight how spatiality and subjectivity intersected in maternal constructions of resilience and self care during the pandemic. They also reveal the need for gender-responsive policies regarding childcare and self-care that acknowledge the multi-layered complexities of mothers’ lives, especially during times of social upheaval or disaster recovery, both of which are on the rise globally.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".