Women Veterans’ definitions of peer support: A qualitative description analysis
Bibliographic record
Abstract
Introduction: Women are the fastest-growing subpopulation of U.S. military Veterans, yet their voices have rarely been used to explore peer support among Veterans. This study defines Veteran peer support from the perspective of women Veterans and aims to increase providers' awareness of the value of peer support for improving Veteran mental health. Methods: = 25) were selected and analyzed. Guided by a qualitative description approach, the researchers used in vivo coding to capture common descriptive language used by the Veterans. Themes were drawn by the primary researcher and audited by a second researcher to increase the findings' trustworthiness. Results: Women Veterans described peer support as a relationship between Veterans based on shared military experience, an understanding of military culture, and similar life challenges. Emerging themes regarding peer support included viewing peers as genuine, trustworthy friends, empathizing over shared struggles, cultivating a safe, non-judgmental, egalitarian space to discuss sensitive topics, using a non-stigmatizing, non-clinical approach, and being a source of fun and social connection. Discussion: Participants provided evidence that peer support is a meaningful, authentic, and accessible means of exchanging emotional and concrete assistance for women Veterans. Mental health providers and program developers working with women Veterans need to understand the importance of peer support in complementing therapy and how it can be essential for women Veterans to discuss sensitive topics such as sexual assault or harassment they experienced in the military.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".