Maternal Mental Health, Child Distress and Family Strain During the COVID-19 Pandemic: Linking the Provincial Longitudinal Cohort with the COVID-19 Impact Survey Data in Canada.
Bibliographic record
Abstract
ObjectiveTo understand the impact of the COVID-19 pandemic on families in Canada by specifically examining the relationship between maternal mental distress (MD), child distress (CD) and family strain (FS) trends over time. We linked the Alberta Pregnancy Outcomes and Nutrition (APrON) longitudinal cohort data and COVID-19 Impact Survey (CIS). ApproachThree waves of CIS (March 2020 to July 2021), collected from APrON longitudinal cohort, were used. Demographic variables from APrON were linked with CIS. Mothers’ depression, anxiety, and/or stress scores were standardized separately for different symptoms, averaged at each wave, and combined as one maternal MD variable (low/medium/high). CD was measured across emotional, conduct, hyperactivity, and peer problem scales (low/high). FS was defined as COVID-19 straining family relationships, including partners, parent-child, and siblings (yes/no). Latent class analyses were performed to identify and categorize membership across the variables. To address the objective, multiple logistic regression models were conducted. ResultsThe sample consisted of 157 participants were included in the study; 19.1% reported FS during COVID-19. Three latent classes were formed for maternal MD: consistently low (36.9%), medium (44.0%), and high (19.1%) across the follow-up period. Two latent classes were formed for CD: consistently low (79.6%) and high (20.4%). When adjusted for COVID-19 related covariates (e.g., maternal worries about child’s well-being/education, family difficulty with childcare/schoolwork) and socioeconomic status, mothers with medium and high levels of maternal MD were at increased odds of experiencing FS during the COVID-19 pandemic compared to those with a low level of distress (medium aOR = 3.90[1.08, 14.03]; high aOR = 4.57[1.03, 20.25]). The adjusted association between child distress and FS was not statistically significant (aOR = 1.75[0.59, 5.20]). ConclusionUnderstanding how MD could affect family strain is important as many families recover from the pandemic. More distressed individuals experience greater FS over time, suggesting this association as a chronic problem. Stakeholders should tailor support systems to longer-term, family-level interventions improving family relationships and maternal-child MHD impacted by COVID-19.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".