New approach to assessing and addressing moral distress in intensive care unit personnel: a case study
Bibliographic record
Abstract
PURPOSE: To test a new approach to address moral distress in intensive care unit (ICU) personnel. METHODS: Using principles of participatory action research, we developed an eight-step moral conflict assessment (MCA) that guides participants in describing the behaviour that they have to implement, the effects this has on them, their current coping strategies, their values in conflict, any other concerns related to the situation, what helps and hinders the situation, new coping strategies, and the effect of the preceding steps on participants. This assessment was tested with eight ICU providers in an 11-bed community ICU. RESULTS: During three one-hour sessions, participants described their moral distress that was caused by the use of ongoing life-support for a patient who the team believed did not prefer this course of care, but whose family was requesting it. Participants experienced frustration and discouragement and coping strategies included speaking to colleagues and exercising. They felt that they were unable to take meaningful action to resolve this conflict. Values that were in conflict in the situation included beneficence and patient autonomy. Based on ranking of helping and hindering factors, the team proposed new strategies including improving consistency of care plans and educating patients' family members and ICU personnel about advance care planning and end-of-life care. After completing this assessment, participants reported less stress and a greater ability to take meaningful action, including some of the proposed new strategies. CONCLUSIONS: We found this new approach to address moral distress in ICU personnel to be feasible and a useful tool for facilitating plans for reducing moral distress.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".