Work stressors, work-family conflict, parents’ depressive symptoms and perceived parental concern for their children’s mental health during COVID-19 in Canada: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Work-related stressors and work-family conflict are important social determinants of mental health. While the impact of these stressors on parents' mental health is well documented, we know comparatively less about their impact on children's mental health. Furthermore, though the COVID-19 pandemic has significantly altered these stressors, particularly with the increase in teleworking, major knowledge gaps persist regarding the association between parents' stressors and perceived parental concern for their children's mental health during the COVID-19 pandemic. Based on the stress contagion perspective, this study tests (1) the mediating role of parents' depressive symptoms with parental concern for their children's mental health, and (2) whether these associations vary depending on whether parents had the opportunity to engage in telework. METHODS: A path analysis was performed from a cross-sectional analytic sample of 780 employed parents in the province of Quebec (Canada). The same model was then stratified by teleworking opportunity. The model's indirect associations were obtained by the bootstrap bias-corrected method with 1,000 replications. RESULTS: The results show that the stressors of work-to-family conflict, increased difficulties in work-family balance since the COVID-19 pandemic, irregular schedules, low esteem derived from work, and job insecurity were all indirectly associated with an increase in parental concern for their children's mental health through increased parents' depressive symptoms. However, some associations differ depending on teleworking status. The indirect associations involving increased difficulties in work-family balance since the COVID-19 pandemic as well as irregular work schedules were observed only in the teleworking group. CONCLUSIONS: This study fills a gap in research on the association between the work-family interface and parental concern for their children's mental health during the COVID-19 pandemic. It highlights the importance of concerted and cohesive action between child health policies and those regarding work and work-family balance to prevent work-related psychosocial risks, particularly considering the post pandemic expanded and persistent reliance on teleworking.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".