An Exploration of Moral Distress Among Nurse Managers In Long Term Care Facilities
Bibliographic record
Abstract
Moral distress is defined as the suffering experienced as a result of situations in which individuals are aware of a moral problem, acknowledge moral responsibility, and make a moral judgment about the correct action to take, yet due to constraints (real or perceived) cannot carry out this action. Thus they believe that they are committing a moral offence by compromising their personal and professional values. The suffering may present as feelings of anger, frustration, guilt and/or powerlessness associated with a decreased sense of well-being. The purpose of this research was to explore the experience and impact of moral distress on Nurse Managers working in long-term care (LTC) organizations. And at the same time to explore the ethical climate within those organizations to discern whether to facilitate or impede the resolution of moral distress. Few studies have explored moral distress in both the Nurse Manager and LTC context. Using a case study research method, the respondents in this study described in detail their experiences of moral distress, the circumstances in which they occurred, and the deleterious effects on their physical, emotional, social, psychological, and spiritual well-being. Among the findings in this study, there were some correlations between the positive ethical climate found in a healthy workplace and lower levels of moral distress, and the power that positive relationships exert in coping with moral distress during and after the situation. There were several coping mechanisms Nurse Managers identified as helpful in dealing with moral distress. However, when the intensity of moral distress reached unbearable levels, and the coping mechanisms seemed to no longer suffice, Nurse Managers would leave their position or their organization. This study also asked participants to consider what advice they would give to new Nurse Managers, the organization’s leaders and the healthcare system as a whole in order to address the issue of moral distress. The respondents identified a number of helpful or potentially helpful recommendations to support new managers, which may aid in developing organizational strategies that could support the wellbeing of Nurse Managers, today and into the future, and may help to reduce staff attrition and burnout.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".