Stories of transition: U.S. Veterans’ narratives of transition to civilian life and the important role of identity
Bibliographic record
Abstract
Introduction: To date, investigations of Veterans' transition to civilian life after military service have tended to focus on the experiences of those with mental or physical health difficulties or on employment challenges and homelessness. This study aimed to gain a deep understanding of Veterans' transition to civilian life, the challenges they face, and the adap-tive and maladaptive ways in which they manage them. Methods: A narrative approach was used to afford the Veterans an opportunity to share their experiences through their transition story. Six male Veterans residing in the Chicagoland area who had left the military between 1 and 12 years earlier were interviewed using a narrative approach. Results: Narrative analysis led to the emergence of three master narratives: narratives of the challenges, narratives of readiness, and narratives of continued military values. The narratives the Veterans shared highlighted not only the importance of practical readiness for transition but also the need for a fundamental addition to how Veteran transition is considered that includes psychological considerations of the impact on identity and the potential for existential crisis. Discussion: Appraising transition only in terms of measurable factors such as employment, living conditions, and health likely over-looks those experiencing psychological challenges and sub-clinical mental health difficulties. The proposed fundamen-tal addition has implications for work with Veterans in various health care settings and for existing transition programs, including a consideration of the role of identity.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".