Social support over time for men and women veterans with and without complex trauma histories.
Bibliographic record
Abstract
Social support is closely linked to health, but little is known about United States (U.S.) veterans' social support over time and factors that may influence their support trajectories. This study investigates social support over time for U.S. men and women Post-9/11 veterans in relation to trauma history and gender. A secondary analysis of longitudinal cohort data from the Survey of Experiences of Returning Veterans (SERV), which employed a repeated-measures longitudinal design using five waves of data (baseline, 3, 6, 9, 12 months) with 672 combat veterans. Results from random intercept multilevel models found no significant gender differences in social support over time. Veterans with complex trauma histories were at risk for lower social support across waves. A stability trend was also observed; specifically, at baseline, veterans who started with high support maintained their level over time whereas veterans who started with deficits in social support remained low over time. Veterans identifying as African American or Latinx, and those with lower annual incomes, reported lower support compared to White and higher-income veterans. Furthermore, low social support was significantly associated with severe posttraumatic stress symptoms and active suicidal ideation across 12 months. SERV utilized a nonrandom sampling method that may reduce generalizability of findings. There is also potential for residual confounding by factors related to both social support levels and time since discharge that were not available in this data set. Findings have implications for developing clinical and community interventions intended to support veterans as they transition back to the community. (PsycInfo Database Record (c) 2023 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".