What Comes after Moral Injury?—Considerations of Post-Traumatic Growth
Bibliographic record
Abstract
Moral injury is a psychological wound resulting from deep-rooted traumatic experiences that corrode an individual’s sense of humanity, ethical compass, and internal value system. Whether through witnessing a tragic event, inflicting injury on others, or failing to prevent a traumatic injury upon others, moral injury can have severe and detrimental psychological and psychosomatic outcomes that may last a lifetime. Post-traumatic experiences do not have to be a permanent affliction, however. From moral injury can come post-traumatic growth—the recovery from trauma in which personal betterment overshadows moral injury. Moral injury may lead to substantial personal growth, improved capacity and resilience. Based on these observations, it seems that from struggles and darkness, there can be positivity and hope. This review will summarize the current concepts of post-traumatic growth and consider potential mechanisms leading to resilience and recovery through post-traumatic growth. These considerations are gaining more importance in light of a growing number of existential threats, such as violent conflicts, natural disasters and global pandemics.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| 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".