Impacts of the COVID-19 Pandemic on Family Mental Health in Canada: Findings from a Multi-Round Cross-Sectional Study
Bibliographic record
Abstract
Pandemic-related disruptions, including school, child care, and workplace closures, financial stressors, and relationship challenges, present unique risks to families’ mental health. We examined the mental health impacts of the coronavirus disease 2019 (COVID-19) pandemic among parents with children <18 years old living at home over three study rounds in May 2020 (n = 618), September 2020 (n = 804), and January 2021 (n = 602). Data were collected using a cross-sectional online survey of adults living in Canada, nationally representative by age, gender, household income, and region. Chi-square tests and logistic regression compared outcomes between parents and the rest of the sample, among parent subgroups, and over time. Parents reported worsened mental health compared with before the pandemic, as well as not coping well, increased alcohol use, increased suicidal thoughts/feelings, worsened mental health among their children, and increases in both negative and positive parent–child interactions. Mental health challenges were more frequently reported among parents with pre-existing mental health conditions, disabilities, and financial/relationship stressors. Increased alcohol use was more frequently reported among younger parents and men. Sustained mental health challenges of parents throughout nearly a year of the pandemic suggest that intervention efforts to support family mental health may not be adequately meeting families’ needs. Addressing family stressors through financial benefit programs and virtual mental health supports should be further explored.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".