Healthcare Workers and COVID-19-Related Moral Injury: An Interpersonally-Focused Approach Informed by PTSD
Bibliographic record
Abstract
The COVID-19 pandemic has resulted in a still-unfolding series of novel, potentially traumatic moral and ethical challenges that place many healthcare workers at risk of developing moral injury. Moral injury is a type of psychological response that may arise when one transgresses or witnesses another transgress deeply held moral values, or when one feels that an individual or institution that has a duty to provide care has failed to do so. Despite knowledge of this widespread exposure, to date, empirical data are scarce as to how to prevent and, where necessary, treat COVID-19-related moral injury in healthcare workers. Given the relation between moral injury and post-traumatic stress disorder (PTSD), we point here to social and interpersonal factors as critical moderators of PTSD symptomology and consider how this knowledge may translate to interventions for COVID-19-related moral injury. Specifically, we first review alterations in social cognitive functioning observed among individuals with PTSD that may give rise to interpersonal difficulties. Drawing on Nietlisbach and Maercker's 2009 work on interpersonal factors relevant to survivors of trauma with PTSD, we then review the role of perceived social support, social acknowledgment and social exclusion in relation to potential areas of targeted intervention for COVID-19-related moral injury in healthcare workers. Finally, building on existing literature (e.g., Phoenix Australia-Centre for Posttraumatic Mental Health and the Canadian Centre of Excellence-PTSD, 2020) we conclude with individual and organizational considerations to bolster against the development of moral injury in healthcare workers during the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".