Bibliographic record
Abstract
Background Moral injury is an emerging explanation of burnout and suicidality, but remains poorly quantified in at-risk practitioners. We hypothesized that COVID-19 pandemic-related moral injury differs between frontline clinicians, genders, age, and country of practice. Methods We conducted an online cross-sectional survey of international physicians, nurses, nurse practitioners, paramedics and respiratory therapists between April and June 2020. We included the adapted version of the Expressions of Moral Injury Scale (EMIS). The primary outcome was differences in moral injury scores between clinician roles. Results Three hundred and two clinicians participated, including physicians (61% [n=184]), nurses (28% [n=85]), and nurse practitioners (5% [n=14]). The median age was 39 (IQR 32-76), females comprised 54% of the respondents, and the majority resided in Canada (n =183 [61%]) or the United States (US; n = 106 [35%]). Emergency medicine (88% [n=265]), and intensive care (6% [n=17]) were the main specialties responding. Median moral injury scores across multiple domains were higher for nurses compared to physicians, as well as for younger, and female respondents. Moral injury scores were also significantly higher for respondents from the United States, the United Kingdom and Australia, compared to Canada. Conclusions Our research suggests that during COVID-19, measures of moral injury differ across roles, gender and place of work. Future research is warranted to better understand the impact of moral injury on clinicians’ psychological well-being during the COVID-19 pandemic.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".