Parental loneliness, parental stress and child mental health during the COVID-19 pandemic: Variations by cumulative socioeconomic risk
Bibliographic record
Abstract
The COVID-19 pandemic has raised concerns about parent and child mental health, especially within disadvantaged families. However, little is known about how parental stress and loneliness during the pandemic influenced their children and no studies have investigated if these associations could vary by socioeconomic status. In July to September 2021, a large representative sample of parents (N = 4,524) in Québec (Canada) reported on aspects of their own mental health and that of their 9–10 year old child. Outcome variables were child externalizing and internalizing symptoms. Exposure variables were changes in parental loneliness and parental stress since the onset of the pandemic. The moderator variable was cumulative socioeconomic risk. Multiple linear regression analyses were executed and adjusted for confounding factors. Child internalizing symptoms were associated with higher levels of parental stress (β = 0.14, p<.001) and loneliness (β = 0.23, p<.001). Child externalizing symptoms were also associated with parental stress (β = 0.13, p<.001) and loneliness (β = 0.18, p<.001). Most of these associations were stronger within the most disadvantaged households. The cross-sectional design does not allow interpretations about causality. Increases in parental stress and loneliness since the onset of the COVID-19 pandemic would be detrimental to parent and child mental health. As these issues were amplified within families experiencing more socioeconomic adversities, our results can inform public policy to support families in times of crisis and direct resources to those most in need.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".