The new frontline: exploring the links between moral distress, moral resilience and mental health in healthcare workers during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Global health crises, such as the COVID-19 pandemic, confront healthcare workers (HCW) with increased exposure to potentially morally distressing events. The pandemic has provided an opportunity to explore the links between moral distress, moral resilience, and emergence of mental health symptoms in HCWs. METHODS: A total of 962 Canadian healthcare workers (88.4% female, 44.6 + 12.8 years old) completed an online survey during the first COVID-19 wave in Canada (between April 3rd and September 3rd, 2020). Respondents completed a series of validated scales assessing moral distress, perceived stress, anxiety, and depression symptoms, and moral resilience. Respondents were grouped based on exposure to patients who tested positive for COVID-19. In addition to descriptive statistics and analyses of covariance, multiple linear regression was used to evaluate if moral resilience moderates the association between exposure to morally distressing events and moral distress. Factors associated with moral resilience were also assessed. FINDINGS: Respondents working with patients with COVID-19 showed significantly more severe moral distress, anxiety, and depression symptoms (F > 5.5, p < .020), and a higher proportion screened positive for mental disorders (Chi-squared > 9.1, p = .002), compared to healthcare workers who were not. Moral resilience moderated the relationship between exposure to potentially morally distressing events and moral distress (p < .001); compared to those with higher moral resilience, the subgroup with the lowest moral resilience had a steeper cross-sectional worsening in moral distress as the frequency of potentially morally distressing events increased. Moral resilience also correlated with lower stress, anxiety, and depression symptoms (r > .27, p < .001). Factors independently associated with stronger moral resilience included: being male, older age, no mental disorder diagnosis, sleeping more, and higher support from employers and colleagues (B [0.02, |-0.26|]. INTERPRETATION: Elevated moral distress and mental health symptoms in healthcare workers facing a global crisis such as the COVID-19 pandemic call for the development of interventions promoting moral resilience as a protective measure against moral adversities.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".