Depression, parenting and the COVID-19 pandemic in Canada: results from three nationally representative cross-sectional surveys
Bibliographic record
Abstract
OBJECTIVES: Depression is associated with problems in functioning in many aspects of life, including parenting. COVID-19 has increased risk factors for depression. We investigated the prevalence of depression among parents during the pandemic and the association with dysfunctional parenting. DESIGN: Canadian nationwide cross-sectional study. SETTING AND PARTICIPANTS: The 2020 and 2021 Surveys on COVID-19 and Mental Health (SCMH) and the Canadian Community Health Survey (CCHS) (2015‒2019). Responding sample sizes for parents were 3121 for the 2020-SCMH; 1574 for the 2021-SCMH and 6076 for the CCHS. PRIMARY OUTCOME MEASURES: All three surveys collected information on symptoms of major depressive disorder (MDD). The SCMH measured harsh parenting. RESULTS: Based on data from the 2021-SCMH collected during wave 3 of COVID-19, 14.4% of fathers and 21.2% of mothers screened positive for MDD. These prevalence estimates were similar to those from the 2020-SCMH during wave 2, but at least two times higher than pre-COVID-19 estimates from the CCHS. Multivariate analyses revealed a linear association between MDD and harsh parenting. COVID-19-related stressors were associated with harsh parenting. Among mothers, feeling lonely or isolated because of COVID-19 was a risk factor for harsh parenting; among fathers, being a front-line worker was a risk factor. Meditation was a protective factor for mothers. CONCLUSIONS: After years of stability, the prevalence of MDD increased substantially among Canadian parents during the pandemic. Ongoing monitoring is vital to determine if elevated levels of depression persist because chronic depression increases the likelihood of negative child outcomes. Programmes aimed at addressing depression and bolstering parenting skills are needed as families continue to face stressors associated with COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".