The Role of Carer-Friendly Workplace Policies and Social Support in Relation to the Mental Health of Carer-Employees
Bibliographic record
Abstract
Carer-employees (CEs) are unpaid carers who are simultaneously working in paid employment. Workplace stress often compounds with caregiving stress to cause negative health effects for CEs. This analysis investigates cross-sectional data of the 2018 Canadian General Social Survey (GSS) to determine whether CEs who experienced work interferences (WIs), including taking time off work, turning down a job offer or promotion, and taking a less demanding job, were associated with poor mental health due to caregiving responsibilities. Carer-friendly work policies (CFWPs) and social support would lower the mental health impact of CEs and moderate the association between WIs and mental health. Of the 23,025 respondents, 4,291 were CEs. A series of multivariate logistic regressions were conducted on various mental health symptoms (e.g., feeling tired, experiencing appetite loss, and having trouble sleeping). Most WIs were positively associated with mental health symptoms. CFWPs, such as flexible scheduling, the option to work part-time, being able to take a leave of absence or an extended leave, and feeling that CFWPs can be taken without negative impacts on one’s career, were negatively associated with at least one mental health symptom caused by the caregiving responsibilities. The option to telework was found to be nonsignificant. Generally, social support was associated with an increased chance of mental health symptoms, apart from help from the community. CEs who worked in workplaces that promoted CFWPs without negative impacts on their careers were less likely to feel anxious when turning down a job offer or promotion. Our study highlights the importance of CFWPs for CEs’ mental health. As the number of CEs increases over time, the need for effective and wide-ranging CFWPs becomes more important.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".