Sources of moral distress for nursing staff providing care to residents with dementia
Bibliographic record
Abstract
The World Health Organization estimates the number of people living with dementia at approximately 35.6 million; they project a doubling of this number by 2030 and tripling by 2050. Although the majority of people living with a dementia live in the community, residential facility care by nursing care providers is a significant component of the dementia journey in most countries. Research has also shown that caring for persons with dementia can be emotionally, physically, and ethically challenging, and that turnover in nursing staff in residential care settings tends to be high. Moral distress has been explored in a variety of settings where nurses provide acute or intensive care. The concept, however, has not previously been explored in residential facility care settings, particularly as related to the care of persons with dementia. In this paper, we explore moral distress in these settings, using Nathaniel's definition of moral distress: the pain or anguish affecting the mind, body, or relationships in response to a situation in which the person is aware of a moral problem, acknowledges moral responsibility, makes a moral judgment about the correct action and yet, as a result of real or perceived constraints, cannot do what is thought to be right. We report findings from a qualitative study of moral distress in a single health region in a Canadian province. Our aim in this paper is to share findings that elucidate the sources of moral distress experienced by nursing care providers in the residential care of people living with dementia.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".