Bibliographic record
Abstract
Moral distress has been explored within a number of nursing contexts, including critical care, neuroscience, and end-of-life decision making. Although the antecedents and consequences of this concept continue to be uncovered, its unique attributes remain ambiguous. This analysis aims to clarify the concept of moral distress, contribute new insights about moral distress to nursing as a whole and to the subspecialty of neuroscience nursing in particular, and enhance advancements in nursing knowledge and practice. Literature published in English between 1987 and 2009 was searched using the Cumulative Index to Nursing and Allied Health Literature and Google Scholar databases. Eleven journal articles were used in the final analysis. Rodgers' evolutionary model of concept analysis was used in this study. Four comprehensive attributes were formulated to describe moral distress in neuroscience nursing: negative feelings, powerlessness, conflicting loyalties, and uncertainty. These attributes are intimately related, holding true meaning only when viewed within the context of one another and with respect to the historical and philosophical underpinnings of nursing praxis. This analysis demonstrates the fluidity, complexity, and multifacetedness of moral distress. Knowledge of the conceptual attributes presented herein will facilitate recognition and validation of personal experiences within the neuroscience nursing community.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".