Identity in turmoil: Investigating the morally injurious dimensions of minority stress
Bibliographic record
Abstract
Background: Sexual and gender minorities (SGMs) are at an increased risk for developing mental health disorders due to their socially stigmatized identities. Minority stress (i.e. discrimination, identity nondisclosure, internalized stigma) has been shown to impact mental health outcomes among SGMs. Both distal and proximal minority stressors may serve as potentially morally injurious events (PMIEs), which may lead to moral injury and trauma/stressor-related symptoms. Critically, minority stress-related moral injury among SGMs has never before been explored using a mixed-methods approach.Methods: Thirty-seven SGM participants with diverse minority identities participated in the study. Using a convergent parallel mixed-methods design, we conducted semi-structured qualitative interviews, performed clinical assessments, and administered a comprehensive battery of quantitative measures. Here, we modified the Moral Injury Event Scale (MIES) for use with SGMs. Qualitative themes were extracted and then converged with MIES scores to investigate differential thematic presentations based on the quantitative intensity of SGM-related PMIEs.Results: Data analysis indicated four core themes related to moral injury among SGMs: shame (internalizing stigma), guilt, betrayal/loss of trust, and attachment injuries (rejection, altered sense-of-self, and social cognition). The qualitative presentation of these themes differed depending on MIES severity. Attachment injuries emerged as a unique core feature of moral injury among SGMs, whereby the remaining core themes align with previous moral injury research. Furthermore, quantitative analyses revealed that the level of exposure to and intensity of minority stress-related PMIEs was positively associated with hazardous alcohol use and trauma-related symptoms.Conclusions: This is the first mixed-methods study to investigate minority stressors as PMIEs, highlighting how these experiences may contribute to symptoms of moral injury among SGMs. Moral injury may serve as a valuable framework for better understanding trauma-related symptoms and mental health disparities among SGMs. These findings have the potential to inform novel treatment interventions aimed at addressing mental health burdens among SGMs.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".