Moral distress and alcohol use among nurses during COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has brought rapid changes, increased stress, and ethical challenges to nurses across the globe. These factors may place nurses at increased risk for developing moral distress and vulnerability to alcohol use. The primary objective of this report was to determine if time with patients diagnosed with COVID-19 increased nurse risk for moral distress and unhealthy alcohol use among nurses in a community hospital during the COVID-19 pandemic. An online survey, consisting of demographic questions, the Measure of Moral Distress in Healthcare Professional tool, the Alcohol Use Disorder Identification Tool, and a single item asking about the amount of time caring for COVID-19 patients was sent to inpatient and emergency department nurses and 57 nurses completed the survey. Nurses were found to be experiencing various levels of moral distress. One-third of the nurses reported an intention to leave their position due to moral distress. One-third of nurses reported risky alcohol use, while 5.3% reported harmful alcohol use. Time spent with COVID patients predicted moral distress and time spent with COVID patients predicted level of alcohol dependence. Moral distress was not a predictor of risky alcohol use. Given the literature on the crescendo effect of moral distress and the nature of alcohol use disorder, the lasting effects on nurses during the COVID-19 pandemic will be important to the profession for years to come. Nursing leadership must commit to implementing resources to help prevent and care for nurses who experience moral distress and alcohol use disorder.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".