A Canadian national survey of informal employed caregivers of older adults with and without dementia: Work and employee outcomes
Bibliographic record
Abstract
Background: The majority of family caregivers (CG) caring for older adults, many of whom have dementia, are employees concurrently contending with the work demands and the stress and conflicts of caregiving. Both employers and CG employees are challenged by the need to address this problem. Method: A cross-sectional Canadian survey was distributed nationally to working informal CGs of older adults in 2015 to 2016. It was designed to investigate the relative predictive roles of caregiving variables, job demands, balancing work and caregiving variables, and work-related factors on work and employee outcomes. Our sample was comprised of employees ( N = 1,839) who were concurrently providing informal care for an older adult with specific attention to those caring for care recipients (CR) with dementia. We employed hierarchical and ordinal multiple regression to examine CG and caregiving characteristics, family and job demands, and balancing job-caregiving variables as predictors of work-related outcomes including work engagement, employment/employee changes index, absenteeism, and intent-to-turnover. Results: After controlling for CGs’ age, sex, and family financial situation, we found dementia demands, job-caregiving conflict, effective manager, and organizational culture were significant predictors of five out of six work and employee outcomes. Role overload was significantly associated with four outcomes. Conclusion: To our knowledge, this is the first study of this size to explore the association of these predictive variables with work-related outcomes and to refine the understanding of the profile of employed CGs of older adults with dementia. Sustaining working CGs of older adults may require new ‘talent management’ approaches rather than simply increasing the number of benefits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| 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".