PTSD symptom clusters and alcohol use among midlife women Veterans
Bibliographic record
Abstract
Introduction: There are high rates of co-occurring posttraumatic stress disorder (PTSD) and alcohol use among women Veterans. Given the growing number of midlife women Veterans utilizing Veterans Health Administration (VHA) care and their unique stressors, the purpose of the current study was to examine the unique relationships between PTSD symptom clusters and alcohol use among this group of women. Methods: = 50.00; 72% Black/African American) completed self-report measures on demographics, alcohol use and behaviours, and PTSD symptoms as part of their intake at a Veterans Affairs (VA) general mental health clinic. Results: Findings indicated that alcohol use was associated with PTSD symptom severity, as well as the intrusions, negative cognitions and mood, and arousal PTSD symptom clusters, but not the avoidance PTSD symptom cluster, after accounting for branch of service. Discussion: Findings of the current study are in line with previous research highlighting the unique relationship between alcohol use and PTSD symptoms. Our sample was primarily Black/African American midlife women, which may limit the generalizability of our findings. Given high rates of alcohol use and mental health symptoms among women at midlife, providers and researchers alike should aim to better understand the intersection of perimenopause/menopause and mental health outcomes to provide comprehensive health care for at-risk Veterans.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".