Mental Health Among Employed Family Caregivers: A Gender-Based Analysis on the Role of Workplace Support
Bibliographic record
Abstract
Abstract Compared to men, women undertake more family caregiving responsibilities, and thus take more toll in health and wellbeing when they are employed. The current study examined the gender difference in mental health among employed family caregivers, focusing on the role of workplace support in balancing work and caregiving roles. Guided by the social role theory and the moderated-mediation model of employment adjustment and mental health, we analyzed a nationally representative data from the 2012 Canada General Social Survey - Caregiving and Care Receiving with a sample of 2,426 participants selected. Moderated-mediation analysis based on the SPSS macro PROCESS 3.3 was used. Women employed family caregivers are more likely to undertake higher intensive caregiving, make more employment adjustment, and further report worse mental health status than their men counterparts. Gender difference was apparent in regards to the workplace support. For women, the moderating effect of workplace support is significant only when there are at least 5 different types of workplace support available at their workplaces, while for men, the moderating effect is significant when there are at least 2-3 different types of workplace support available. Women employed family caregivers experience worse mental health when employment adjustment is needed for their care responsibility. Findings have implications for providing workplace support for family caregivers given that women benefit less from workplace support compared to men. Further study is needed to explore the impact of various types of workplace support for women employed family caregivers, and to provide tailored support to them.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".