Moving from conceptual ambiguity to knowledgeable action: using a critical realist approach to studying moral distress
Bibliographic record
Abstract
Moral distress is a phenomenon that has been receiving increasing attention in nursing and other health care disciplines. Moral distress is a concept that entered the nursing literature - and subsequently the health care ethics lexicon - in 1984 as a result of the work done by American philosopher and bioethicist Andrew Jameton. Over the past decade, research into moral distress has extended beyond the profession of nursing as other health care disciplines have come to question the impact of moral constraint on individual practitioners, professional practice, and patient outcomes. Along with increased interest in the phenomenon of moral distress have come increasing critiques - critiques that in their essence point to a serious lack of conceptual clarity in the definition, study, and application of the concept. Foundational to gaining conceptual clarity in moral distress in order to develop strategies to prevent and ameliorate the experience is a careful revisiting of the epistemological assumptions underpinning our knowledge and use of the concept of moral distress. It is our contention that the conceptual challenges reveal flaws in the original understanding of moral distress that are based on an epistemological stance that holds a linear conception of cause and effect coupled with a simplistic perspective of 'constraint' and 'agency'. We need a more nuanced approach to our study of moral distress such that our ontological and epistemological stances help us to better appreciate the complexity of moral agents acting in organizational contexts. We believe that critical realism offers such a nuanced approach.
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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 | 0.040 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.146 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".