New working conditions and consequences on activity of home healthcare workers
Bibliographic record
Abstract
Home healthcare is steadily growing in Europe. There are a number of reasons for this development: aging population, rising hospital costs, preference to stay in one's own home. Nevertheless, it has been known that home healthcare workers are frequently exposed to a variety of potentially serious occupational hazards. Furthermore, emotional labor is frequently high in this profession. This paper describes an ergonomic study conducted at a home healthcare service. The research focuses on analyzing working conditions of home healthcare aides and nurses, as well as the impacts of their work in terms of job satisfaction, well-being, emotions at work, relationships with the others and occupational stress. The study show that employee strategies are specifically centered around preserving the relationship between patients and workers and coping with the job demands. This paper also shows that home healthcare workers express emotions and conceal them from others. Finally, recommendations discussed with the manager and workers to improve working conditions in this sector led to practical proposals: for example, implementing certain equipment items better suited to difficult care, encouraging assistance between healthcare workers when operations require this through adequate organizational measures, extending work emotion-focused discussion groups with management involvement.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".