The Unique Impacts of COVID-19 on Low-Income Canadian Mother’s Mental Health Profiles: A Latent Transition Analysis
Bibliographic record
Abstract
There is evidence of an overall decline in maternal mental health in the wake of the COVID-19 pandemic. However, there is also heterogeneity in maternal responses. A latent transition analysis was conducted to identify profiles of anxiety, depression, and stress among 289 low-income mothers. Using these identified profiles, we examined the transitional patterns between profiles before and during COVID-19 and the sociodemographic and familial factors related to these profiles. A three-profile solution was identified prior to COVID-19, and a four-profile solution during COVID-19, with some profiles exhibiting qualitatively different defining characteristics. Latent transition analyses found diverse patterns of mental health shifts after the onset of COVID-19. However, mothers with better mental health prior to COVID-19 tended to have the most stable mental health during COVID-19. In contrast, mothers who were highly stressed prior to COVID-19 were equally likely to improve or decline after the onset of the pandemic. In addition, the relationships between ethnicity, parenting practices, child temperament, and mental health were significantly related to maternal mental health. These findings describe mothers' experiences and areas where policymakers and practitioners can tailor support to low-income mothers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".