Injured female Veterans’ experiences with community reintegration: a qualitative study
Bibliographic record
Abstract
Introduction: Reintegration back into civilian life post-deployment can be difficult for military Veterans, particularly those who have physical and psychological injuries. Research indicates that male and female Veterans may experience reintegration differently as a result of their deployment experiences and gender-specific social role expectations. Limited research specific to female Veterans’ reintegration experiences exists in the empirical literature. Therefore, the purpose of this qualitative study was to better understand community reintegration experiences among injured female Veterans. Methods: Phenomenology guided the data collection and analysis. NVivo was used to aid in organization and analysis of the data. An iterative clustering process was used to identify meaning units, resulting in categories and themes that best represented the participants’ experiences. Bracketing procedures were used to account for researcher bias. Results: Three categories and multiple themes emerged from the qualitative analysis: category 1, community reintegration meaning; category 2, perception of community reintegration, which had three themes – (1) reintegration is harder than expected, (2) reintegration is a process, and (3) reintegration involves finding a new normal – and category 3, women’s experience post-deployment, which had four themes – (1) society’s misguided perceptions of women in the military, (2) readjusted or redefined roles and responsibilities as a woman, (3) lingering effects of military sexual trauma, and (4) lack of female-specific services. Discussion: This study suggests a need for female-specific programs within US Department of Veterans Affairs and civilian-based organizations providing services to Veterans. In addition, allied health professionals are encouraged to assess community reintegration to allow for more individualized, long-term transition plans for female Veterans reintegrating into civilian life.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".