The independent and combined impact of moral injury and moral distress on post-traumatic stress disorder symptoms among healthcare workers during the COVID-19 pandemic
Bibliographic record
Abstract
Background: Healthcare workers (HCWs) across the globe have reported symptoms of Post-Traumatic Stress Disorder (PTSD) during the COVID-19 pandemic. Moral Injury (MI) has been associated with PTSD in military populations, but is not well studied in healthcare contexts. Moral Distress (MD), a related concept, may enhance understandings of MI and its relation to PTSD among HCWs. This study examined the independent and combined impact of MI and MD on PTSD symptoms in Canadian HCWs during the pandemic.Methods: HCWs participated in an online survey between February and December 2021, with questions regarding sociodemographics, mental health and trauma history (e.g. MI, MD, PTSD, dissociation, depression, anxiety, stress, childhood adversity). Structural equation modelling was used to analyze the independent and combined impact of MI and MD on PTSD symptoms (including dissociation) among the sample when controlling for sex, age, depression, anxiety, stress, and childhood adversity.Results: A structural equation model independently regressing both MI and MD onto PTSD accounted for 74.4% of the variance in PTSD symptoms. Here, MI was strongly and significantly associated with PTSD symptoms (β = .412, p < .0001) to a higher degree than MD (β = .187, p < .0001), after controlling for age, sex, depression, anxiety, stress and childhood adversity. A model regressing a combined MD and MI construct onto PTSD predicted approximately 87% of the variance in PTSD symptoms (r2 = .87, p < .0001), with MD/MI strongly and significantly associated with PTSD (β = .813, p < .0001), after controlling for age, sex, depression, anxiety, stress, and childhood adversity.Conclusion: Our results support a relation between MI and PTSD among HCWs and suggest that a combined MD and MI construct is most strongly associated with PTSD symptoms. Further research is needed better understand the mechanisms through which MD/MI are associated with PTSD.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".