The Resilience and Socioeconomic Status of Caregivers and School-Aged Children During the COVID-19 Pandemic
Bibliographic record
Abstract
Objectives: Conducted during the height of the COVID-19 pandemic in Ontario, Canada, the purpose of this study was to investigate the relationships between (1) caregiver and child resilience; and (2) socioeconomic status (SES) and the resilience of both caregivers and children. Background: The COVID-19 pandemic introduced many unanticipated challenges for school-aged children and caregivers and navigating them required tremendous resilience. The degree to which the resilience of family members influenced one another or how it was related to economic hardships tied to the pandemic, particularly for members of low SES families, remains poorly understood. Methods: Online surveys were administered to 22 caregivers ( M age = 40.45 years; SD = 4.55) and 27 children ( M age = 8.19 years; SD = 1.14) to measure self-reported resilience and SES. Correlational analyses were conducted to assess both objectives. Results: Analyses detected no significant relationships between the resilience of caregivers and children, nor the resilience and SES of these groups. However, trends revealed that caregivers who reported “normal” levels of resilience had a higher average income than those who reported low levels of resilience. Conclusions: There is insufficient evidence to suggest a relationship between the resilience of caregivers and their children, but income levels may play a role in the resilience of caregivers. Implications: Targeting factors that help families with low SES navigate adversities, including those related to income, need to be identified and better understood to ensure a proactive and equitable public health response in future phases of the pandemic or similar situations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".