Moral Emotions in Palliative Care: Empirical Insights from Austria and Canada
Bibliographic record
Abstract
Research aims: Moral emotions as one specific group of emotions play a vital role in delivering palliative care as e.g. ethical issues and moral distress belong to daily routine. Moral emotions are oriented to the welfare of other persons or the society as a whole. To better understand moral emotions in Palliative Care the aims of the presented study are to ana- lyze care situations from Austria and Canada in different care settings and identify families of moral emotions on one hand and describe influencing contextual factors on the other hand. Methods: Within a qualitative study design a reanalysis of Austrian narratives on ethical issues and Canadian narra- tives on moral distress were conducted. Data in Austria encompass 36 narratives that were generated through qual- itative questionnaires in nursing homes. Canadian data are based on qualitative interviews with home care palliative specialists and encompass 47 critical incidents. The reanal- ysis of data was conducted with narrative analysis. Results: Preliminary results show that moral emotions in palliative care can be found in families around “empathy and relatedness”, “sadness, isolation and bereavement”, “anger, frustration and powerlessness”, “guilt and shame” and “being touched and feel close”. Contextual factors influencing moral emotions can be summarized as “suffer- ing and decline of client”, “expectations and dynamics of family”, “structural conflicts and power issues” and “lack of resources and information”. Conclusion: The diversity of moral emotions reflects the everyday experiences in palliative care. It became obvious that most of the moral emotions that have been expressed appear to be interconnected within a bundle of other emo- tions. Contextual factors influencing moral emotions in pal- liative care are relatively independent of care settings. In Palliative Care moral emotions and their contextual factors constitute an important source of insight for reflection in organizational ethics.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".