Moral distress: Developing strategies from experience
Bibliographic record
Abstract
BACKGROUND: Moral distress was first described by Jameton in 1984, and has been defined as distress experienced by an individual when they are unable to carry out what they believe to be the right course of action because of real or perceived constraints on that action. This complex phenomenon has been studied extensively among healthcare providers, and intensive care professionals in particular report high levels of moral distress. This distress has been associated with provider burnout and associated consequences such as job attrition, with potential impacts on patient and family care. There is a paucity of literature exploring how middle and late career healthcare providers experience and cope with moral distress. OBJECTIVES: We explore the experience of moral distress and the strategies and resources invoked to mitigate that distress in mid- and late-career healthcare providers practicing in paediatric intensive care, in order to identify ways in which the work environment can build a culture of moral resilience. RESEARCH DESIGN: An exploratory, qualitative quality improvement project utilizing focus group and semi-structured interviews with pediatric intensive care front-line providers. PARTICIPANTS: Mid-and-later career (10 + years in practice) pediatric intensive care front line providers in a tertiary pediatric hospital. RESEARCH CONTEXT: This work focuses on paediatric intensive care providers in a single critical care unit, in order to explore the site-specific perspectives of health care providers in that context with respect to moral distress coping strategies. ETHICAL CONSIDERATIONS: The study was approved by the Quality Management Office at the institution; consent was obtained from participants, and no identifying data was included in this project. FINDINGS: Participants endorsed perspective-building and described strategies for positive adaptation including; active, reflective and structured supports. Participants articulated interest in enhanced and accessible formal supports. DISCUSSION: Findings in this study resonate with the current literature in healthcare provider moral distress, and exposed ways in which the work environment could support a culture of moral resilience. Avenues are described for the management and mitigation of moral distress in this setting. CONCLUSION: This exploratory work lays the groundwork for interventions that facilitate personal growth and meaning in the midst of moral crises in critical care practice.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".