Potential Circumstances Associated With Moral Injury and Moral Distress in Healthcare Workers and Public Safety Personnel Across the Globe During COVID-19: A Scoping Review
Bibliographic record
Abstract
Healthcare workers (HCWs) and public safety personnel (PSP) across the globe have continued to face ethically and morally challenging situations during the COVID-19 pandemic that increase their risk for the development of moral distress (MD) and moral injury (MI). To date, however, the global circumstances that confer risk for MD and MI in these cohorts have not been systematically explored, nor have the unique circumstances that may exist across countries been explored. Here, we sought to identify and compare, across the globe, potentially morally injurious or distressful events (PMIDEs) in HCWs and PSP during the COVID-19 pandemic. A scoping review was conducted to identify and synthesize global knowledge on PMIDEs in HCWs and select PSP. Six databases were searched, including MEDLINE, EMBASE, Web of Science, PsychInfo, CINAHL, and Global Health. A total of 1,412 articles were retrieved, of which 57 articles were included in this review. These articles collectively described the experiences of samples from 19 different countries, which were comprised almost exclusively of HCWs. Given the lack of PSP data, the following results should not be generalized to PSP populations without further research. Using qualitative content analysis, six themes describing circumstances associated with PMIDEs were identified: (1) Risk of contracting or transmitting COVID-19; (2) Inability to work on the frontlines; (3) Provision of suboptimal care; (4) Care prioritization and resource allocation; (5) Perceived lack of support and unfair treatment by their organization; and (6) Stigma, discrimination, and abuse. HCWs described a range of emotions related to these PMIDEs, including anxiety, fear, guilt, shame, burnout, anger, and helplessness. Most PMIDE themes appeared to be shared globally, particularly the 'Risk of contracting or transmitting COVID-19' and the 'Perceived lack of support and unfair treatment by their organization.' Articles included within the theme of 'Stigma, discrimination, and abuse' represented the smallest global distribution of all PMIDE themes. Overall, the present review provides insight into PMIDEs encountered by HCWs across the globe during COVID-19. Further research is required to differentiate the experience of PSP from HCWs, and to explore the impact of social and cultural factors on the experience of MD and MI.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 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".