Using evidence-based debriefing to combat moral distress in critical care nurses: A pilot project
Bibliographic record
Abstract
Objective: Moral distress (MD) is a problem for nurses that may cause despair or disempowerment. MD can have consequences like dissatisfaction or resignation from the nursing profession. Techniques such as evidence-based debriefing may help nurses with MD. Creating opportunities for critical care nurses to debrief about their MD might equip them with the tools needed to overcome it. Measuring MD by using the Moral Distress Thermometer (MDT) could provide insight into how debriefings help nurses. The purpose of this pilot project was to examine the impact of evidence-based debriefing sessions on critical care nurses’ sense of MD.Methods: This pilot project used a quasi-experimental, one-group, before-during-after design. Critical care nurses (N = 21) were recruited from one unit at a large academic medical center. Four debriefing sessions were held every 2 weeks. Participants completed the MDT 2 weeks before the first session, at the end of each session they attended, and 1 month after the debriefing sessions.Results: In the pilot project, participants felt that debriefing was helpful by increasing their self-awareness, giving them time to commune with colleagues, and encouraging them to improve self-care habits; however, MDT scores did not change significantly when comparing pre with post intervention scores (t(12) = 0.78, p = .450).Conclusions: The use of debriefing may help nurses gain self-awareness of MD and it may offer nurses strategies to build moral resilience.
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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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".