Impacts of the COVID-19 Pandemic on Children and Families from Marginalized Groups: A Qualitative Study in Kingston, Ontario
Bibliographic record
Abstract
The COVID-19 pandemic has been associated with unprecedented changes to societal structure. School closures, unstable employment, and inaccessible health services have caused enormous disruptions to child and family wellbeing. This study identifies major themes illustrating how child and family wellness were impacted by COVID-19, including unique effects experienced by Indigenous families. In-depth interviews were conducted with key informants (n = 31) recruited from organizations providing healthcare and social services in Kingston, Ontario. Interview transcripts and written survey responses were analyzed using a phenomenological approach to explore themes related to child and family wellbeing. Three major themes identified include school closures, home safety, and outdoor spaces. School closures were generally reported as negatively impacting learning and social development; however, school closures allowed for some Indigenous children to be removed from a colonized education system, contributing to cultural and spiritual growth. Second, respondents reported increased severity and frequency of domestic violence, which negatively impacted child wellbeing. Third, the closure of public outdoor spaces created barriers to maintaining good physical health for children. This study recommends the prioritization of (1) child learning and development by avoiding school closures in pandemic settings and (2) the safety of Indigenous students by decolonizing education. To address the increased exposure to adverse childhood experiences (ACEs) during COVID-19, we recommend improved training for identifying and reporting domestic violence amongst service providers. Our study also reflects the broader need to redefine “essential services”, considering culturally specific services for Indigenous Peoples.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".