Working in Clients' Homes: The Impact on the Mental Health and Well-Being of Visiting Home Care Workers
Bibliographic record
Abstract
The purpose of this paper is to examine the effects of working in clients' homes on the mental health and well-being of visiting home care workers. This paper reports the results of a survey of 674 visiting staff from three non-profit home care agencies in a medium-sized city in Ontario, Canada. Survey results are also complimented by data from 9 focus groups with 50 employees. For purposes of this study, home care workers include visiting therapists, nurses, and home support workers. Mental health and well-being is measured by three dependent variables: stress; job stress; and intrinsic job satisfaction. Multiple least squared regression analyses show several structural, emotional, physical, and organizational working conditions associated with the health and well-being of visiting home care workers. Overall, results show that workload, difficult clients, clients who take advantage of workers, sexual harassment, safety hazards, a repetitious job, and work-related injuries are associated with poorer health. Being fairly paid, having good benefits, emotional labour, organizational support, control over work, and peer support are associated with better health. Results suggest that policy change is needed to encourage healthier work environments for employees who work in clients' homes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".