Moral distress: an emerging problem for nurses in long-term care?
Bibliographic record
Abstract
Nurses and other professional caregivers are increasingly recognising the issue of moral distress and the deleterious effect it may have on professional work life, staff recruitment and staff retention. Although the nursing literature has begun to address the issue of moral distress and how to respond to it, much of this literature has typically focused on high acuity areas, such as intensive care nursing. However, with an ageing population and increasing demand for resources and services to meet the needs of older people, it is likely that nurses in long-term care are going to be increasingly affected by moral distress in their work. This paper briefly reviews the literature pertaining to the concept of moral distress, explores the causes and effects of moral distress within the nursing profession and argues that many nurses and other healthcare professionals working with older persons may need to become increasingly proactive to safeguard against the possibility of moral distress.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".