“You get used to a certain kind of horrible” but the “wrong” kind of horrible leads to moral distress: an interpretive description of moral distress in oncology nursing.
Bibliographic record
Abstract
Moral distress (MD) is frequently discussed in the nursing literature, but few studies include or focus on oncology nurses and those that do have inconsistent findings. Oncology care is distinct from other areas of practice in ways that are significant for the development of MD. This study, the first to focus on MD in Canadian oncology nurses, employed interpretive description to understand the experience of MD, the role of contextual factors, nurses’ responses to MD and perspectives on strategies to mitigate MD. Semi-structured interviews were conducted (via telephone or FaceTime) with 25 oncology nurses, recruited from the Canadian Association of Nurses in Oncology and via social media. Moral distress developed in a complex, non-linear multi-factorial fashion and was highly contextually situated. The experience and development of MD are described in the theme “’ You get used to a certain kind of horrible’… but the ‘wrong’ kind of horrible leads to moral distress.” Themes that describe the context of MD include “oncology nursing is hard,” “you can find your niche,” and “oncology nurses know.” “Humanness” was evident in the development of MD, responses to MD, suggested changes in practice, and is proposed as a generative mechanism for both the rewards and challenges of MD. Recommendations for attending to humanness in practice, considering new approaches to nursing and ethics education, and expanding organizational supports derive from the findings. Future research should consider the impact of interventions that support humanness in practice and examine the role of humanness in other settings and health care provider and administrator populations.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.056 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| 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".