Experiences of moral distress by privately hired companions in Ontario’s long-term care facilities
Bibliographic record
Abstract
PURPOSE: To explore long-term residential care provided by people other than the facilities' employees. Privately hired paid "companions" are effectively invisible in health services research and policy. This research was designed to address this significant gap. There is growing recognition that nursing staff in long-term care (LTC) residential facilities experience moral distress - a phenomenon in which one knows the ethically right action to take, but is systemically constrained from taking it. To date, there has been no discussion of the distressing experiences of companions in LTC facilities. This paper explores companions' moral distress. DESIGN: Data was collected using weeklong rapid ethnographies in seven LTC facilities in Southern Ontario, Canada. A feminist political economy analytic framework was used in the research design and in the analysis of findings. FINDINGS: Despite the differences in their work tasks and employment conditions, structural barriers can cause moral distress for companions. This mirrors the impacts experienced by nurses that are highlighted in the literature. Though companions are hired in order to fill care gaps in the LTC system, they too struggle with the current system's limitations. The hiring of private companions is not a sustainable or equitable solution to under-staffing and under-funding in Canada's LTC facilities. VALUE: Recognizing moral distress and the impact that it has on those providing LTC is critical in terms of supporting and protecting vulnerable and precarious care workers and ensuring high quality care for Canadians in LTC.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".