Employment Adjustment and Mental Health of Employed Family Caregivers in Canada
Bibliographic record
Abstract
In Canada, with the population aging, the need of family caregiving to older adults is increasing. Family caregivers make employment adjustment in order to fulfil caregiving responsibility. However, the studies on the family caregivers’ mental health outcomes associated with employment adjustment are limited. Based on the role theory and stress process model, the current study examined the relationship between employment adjustment and mental health outcomes among family caregivers, and also tested the functions of family-to-work role conflict and workplace support in this relationship. Data were drawn from the 2012 Canada General Social Survey Cycle 26: Caregiving and Care Receiving, which provided a sample of 1,696 employed family caregivers. Hierarchical linear regression and conditional process analysis were used to examine the relationship among employment adjustment, mental health, family-to-work role conflict, and workplace support. The analysis results revealed that employment adjustment is significantly associated with negative mental health outcomes, including worse self-rated mental health, more psychological symptoms, and higher life and caregiving stress level. In addition, the mediating effect of family-to-work role conflict was confirmed, such that family-to-work role conflict mediates the association between employment adjustment and mental health outcomes. Furthermore, the moderating effect of workplace support was identified in the relationship between employment adjustment and family-to-work role conflict. Despite some limitations, current study contributes to the existing body of literature on the mental health outcomes of family caregivers by examining the function of employment adjustment, family-to-work role conflict and workplace support at the same time. The study results also call for greater attention to provide caregiver-friendly workplace support to family caregivers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".