Direct and indirect effects of mindfulness, PTSD, and depression on self-stigma of mental illness in OEF/OIF veterans.
Bibliographic record
Abstract
OBJECTIVE: Two of the most common and costly mental health diagnoses among military veterans who served in the post-9/11 conflicts in Afghanistan and Iraq are posttraumatic stress disorder (PTSD) and depression, but over half of veterans who screen positive for these problems do not seek treatment. A key barrier is self-stigma of mental illness. Mindfulness has shown promise as an explanatory variable in the context of mental health symptoms and self-stigma, but these associations are underexplored in the veterans' literature. This study examines direct and indirect effects among mindfulness, PTSD and depression, and self-stigma in post-9/11-era military veterans. METHOD: A sample of 577 veterans from 3 large American cities completed surveys capturing mindfulness, symptoms of PTSD and depression, and self-stigma. A structural equation modeling approach was used to examine direct and indirect effects among study variables. RESULTS: Mindfulness was associated with less PTSD and depression and indirectly with less self-stigma through the PTSD pathway. PTSD was associated with more depression and self-stigma, and depression was not significantly associated with self-stigma. CONCLUSION: PTSD is strongly associated with self-stigma in military veterans, many of whom do not seek mental health treatment. Findings show that mindfulness is a promising intervention target for reducing symptoms of PTSD directly and reducing associated self-stigma of mental illness indirectly. Additional investigation of links between mindfulness, PTSD and depressive symptoms, and self-stigma in military veterans is warranted. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".