Organizational Change and the Health and Well-Being of Home Care Workers
Bibliographic record
Abstract
Objective: The objective of this research is to study the impact of health care restructuring and other organizational changes on the mental and physical health of home care workers. Methods: This study covers 11 agencies and 7 union locals. We interviewed 59 key decision-makers, 171 workers in 29 focus groups, and surveyed 1, 311 workers (70% response rate). Qualitative data are analyzed for themes and quantitative data analysis consists of descriptive statistics and associations between variables. Results: The restructuring of the health care sector and organizational change have increased stress levels and musculoskeletal disorders of home care workers. Physical health problems among this workforce are much higher than the comparable group in the Canadian population. Restructuring and organizational change are significant factors in decreasing job satisfaction, while increasing absenteeism rates, fear of job loss, and propensity to leave. Conclusions: Occupational health problems experienced by these workers are preventable. It is important to acknowledge that occupational stress can result from incremental changes in the work and external work environment, affecting physical health, job dissatisfaction, absenteeism, and propensity to leave. Sufficient government funding to provide services, avoiding continuous changes in the work environment, and creating supportive work environments can positively contribute to workers' health.
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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 | 0.000 | 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 | 0.000 |
| 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".