The process of moral distress development: A virtue ethics perspective
Bibliographic record
Abstract
This theoretical paper proposes a new perspective to understand the moral distress of nurses more fully, using virtue ethics. Moral distress is a widely studied subject, especially with respect to the determination of its causes and manifestations. Increasing the theoretical depth of previous work using ethical theory, however, can create new possibilities for moral distress to be explored and analyzed. Drawing on more recent work in this field, we explicate the conceptual framework of the process of moral distress in nurses, proposed by Ramos et al., using MacIntyrean virtue ethics. Our analysis considers the experience of moral distress in the context of a practice, enabling the adaptation of this framework using virtue ethics. The adoption of virtue ethics as an ethical perspective broadens the understanding of the complexity of nurses’ experiences of moral distress, since it is impossible to create a ready model that can cover all possibilities. Specifically, we describe how identity, social context, beliefs, and tradition shape moral discomfort, uncertainty, and sensitivity and how virtues inform moral judgments. Individuals, such as nurses, who are involved in a practice have a narrative history and a purpose ( telos) that guide them in every step of the process, especially in moral judgment. It is worth emphasizing that the process described is supported by the formation of moral competence that, if blocked, can lead to moral distress and deprofessionalization. It is expected that nurses seek to achieve the internal good of their practice, which legitimizes their professional practice and supports them in moral decision-making, preventing 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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| 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".