Consequences of Moral Distress in the Intensive Care Unit: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Moral distress is common among personnel in the intensive care unit, but the consequences of this distress are not well characterized. OBJECTIVE: To examine the consequences of moral distress in personnel in community and tertiary intensive care units in Vancouver, Canada. METHODS: Data for this study were obtained from focus groups and analysis of transcripts by themes and sub-themes in 2 tertiary care intensive care units and 1 community intensive care unit. RESULTS: According to input from 19 staff nurses (3 focus groups), 4 clinical nurse leaders (1 focus group), 13 physicians (3 focus groups), and 20 other health professionals (3 focus groups), the most commonly reported emotion associated with moral distress was frustration. Negative impact on patient care due to moral distress was reported 26 times, whereas positive impact on patient care was reported 11 times and no impact on patient care was reported 10 times. Having thoughts about quitting working in the ICU was reported 16 times, and having no thoughts about quitting was reported 14 times. CONCLUSION: In response to moral distress, health care providers experience negative emotional consequences, patient care is perceived to be negatively affected, and nurses and other health care professionals are prone to consider quitting working in the intensive care unit.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".