Debriefing and Reflective Interventions to Address Moral Distress: A Narrative Review
Bibliographic record
Abstract
Moral distress is a common phenomenon found in all areas of nursing practice with a high prevalence in specialties such as critical care nursing. The under management of moral distress is associated with the development of burnout, issues with nursing turnover, and patient safety concerns. Identification of effective interventions to address moral distress remains a novel topic of investigation. The aim of this project was to explore the use of debriefings and reflective practices to address and alleviate moral distress. The population of interest included nurses working in all acute care areas including adult and pediatric populations, with a focus on critical care. A narrative literature review was completed using a combination of both quantitative and qualitative studies. Database searches were conducted on both MEDLINE and CINAHL. A total of 10 studies were included in the review. The majority of the studies utilized interventions with both an educational and reflective or debriefing component. A variety of approaches were used in relation to intervention implementation including timing, the profession of both the participants and facilitators, moral distress measurement instrument, and intervention duration and frequency. Most of the studies did not find a significant change in moral distress levels or severity between pre-and post-implementation of the moral distress intervention. No longitudinal studies were conducted to assess the long-term implementation of programs or moral distress measurements. Given the high prevalence and cost of moral distress in the nursing profession more investigation into interventions is required.
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".