Dual-earning parents’ work-family balance and time with children: the moderating effects of gender and age
Bibliographic record
Abstract
Achieving work-family balance is a challenge for many families in Canada, especially for dual-earner families with children in the household. Prior research regarding the predictors of work-family balance has mainly focused on work characteristics; therefore, the current study aimed to assess the predictive effect of a key family characteristic -- quality time with children -- on work-family balance. The two objectives of this study were: (a) to describe the association between time with children and parents’ work-family balance among Canadian dual-earner parents, and (b) to understand the effects of age of the youngest child, parent’s gender, and parent’s age on the association between work-family balance and quality time with children. This study used cross-sectional national time-use data from the General Social Survey (GSS) 2010, Cycle 24. T-test and logistic regression analyses were used to address the two research objectives, and all analyses were weighted. Findings indicated that work-family balance was negatively associated with quality time with children. Age of the youngest child, parent’s gender, and parent’s age were found to moderate the effect of quality time with children on work-family balance: The negative effect of quality time with children on work-family balance was stronger for parents who had a youngest child of an older age than for those who had a youngest child of a younger age, for parents who were older than for those who were younger, and for mothers more than for fathers. Findings of this study can add strength to the understanding of work-family balance of Canadian parents and have implications for helping Canadians balance their paid work and family life demands. As well, the findings indicate a more nuanced exploration of how parents’ relationships with their children affect their experience of work-family balance is needed in future research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".