Predictors of negative moral appraisals and their association with symptoms of post‐traumatic stress and depression in the context of COVID‐19 related stressors
Bibliographic record
Abstract
Research on moral injury (MI) suggests that negative moral appraisals of stressful events can impact mental health in high-stakes occupational contexts (e.g., military). Few studies have examined these associations in the general population, limiting the generalisability of findings. Furthermore, factors that may predispose an individual to adverse outcomes in the context of moral stressors remain largely unknown. The objectives of this study were to (1) explore the applicability of the MI construct to stressors experienced by the general public during the COVID-19 pandemic; (2) explore how trait differences in sense of duty, religiosity/spirituality, anxiety sensitivity, and guilt, shame, and anger, predict negative moral appraisals of COVID-19 stressors. Participants (n = 355) completed an online survey assessing exposure to and appraisals of COVID-19 stressors, mental health symptoms, and dispositional characteristics (i.e., trait emotions, anxiety sensitivity, sense of duty, religiosity/spirituality). Path analysis revealed specific indirect associations between self-based moral appraisals and posttraumatic stress disorder (PTSD) and depression through guilt, and between both self- and other-based moral appraisals and PTSD and depression through anger. Number of COVID-19 stressors had no influence on associations. Sense of duty, reparative guilt, and anxiety sensitivity best predicted negative moral appraisals. Findings partially support the applicability of the MI construct outside the occupational context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".