Treatment stigma mediates relationships between morally injurious events and depression, PTSD and anxiety symptoms
Bibliographic record
Abstract
Background: Morally injurious events (MIEs), encompassing personal transgressions, witnessing others commit transgressions, or experiencing betrayal by leaders, can conflict with one's moral/ethical principles, evoking outrage and profound mistrust. Although MIEs are associated with depression, PTSD, and anxiety, the mechanisms linking MIEs to psychiatric symptomatology remain unclear, especially among civilians in times of collective trauma.Objective: This study explored one potential mechanism: stigma toward mental-health treatment, which can deter help-seeking and exacerbate guilt, shame, and mistrust.Method: We focused on civilians (N = 1,052) exposed to MIEs in conflict zones in southern and northern Israel following the 7 October 2023 attack. Participants were recruited using an online platform and assessed for depression, PTSD, and anxiety symptoms. We hypothesised that stigma toward treatment would mediate relationships between MIE exposure levels and depression, PTSD, and anxiety symptoms.Results: Results showed high MIE exposure levels and symptomatology among civilians in conflict zones. Moreover, we found significant indirect effects of stigma toward treatment on all three symptom types.Conclusions: Our findings suggest that while MIEs directly link to symptoms, stigma toward treatment plays a significant role in understanding this link. These findings emphasise the importance of addressing stigma toward treatment for individuals experiencing MIEs and underscore the need for targeted interventions in conflict zones.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".