Being a woman in a man’s military: The impact of military service on the lives of older U.S. women Veterans
Bibliographic record
Abstract
Introduction: Women Veterans represent a growing and unique minority group among the older (aged ≥65 y) U.S. Veteran population. Research indicates that the impact of serving in the military on Veterans' health and well-being varies by gender. Limited research exists specific to women Veterans' experiences of their time in the military and its long-term impact on their health and well-being. Therefore, the purpose of this qualitative study was to explore the experiences of older U.S. women Veterans regarding their experiences and perceptions of their time in military service and its overall impact on their lives. Methods: Data were gathered from five older women Veterans using semi-structured interviews and analyzed using a thematic coding process. Results: Four main themes emerged from the qualitative analysis: 1) family military history, 2) being treated differently and proving themselves, 3) making the most of opportunities, and 4) lasting personal strengths. Discussion: Findings from this study show that despite enduring negative experiences during military service because of their gender, participants credited the military with having an overall positive impact on their lives in the long term. This study suggests that health care professionals would benefit from gaining an understanding of the historical narrative of women Veterans. Interventions and services that tap into positive aspects of military service, as identified by older women Veterans (i.e., personal strengths such as resilience and pride), may help promote the health and well-being of this population.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".