Who am I? A qualitative exploration of the identities of spouses/partners of UK Armed Forces Veterans
Bibliographic record
Abstract
Introduction: Events such as frequent separation, relocation, and the inherent risks of military service may negatively affect partner health/well-being and sense of identity. While research has attempted to understand how the identities of personnel change after transitioning out of the military community, there is little focus on partner identities. Methods: Qualitative interviews with 37 current and former partners (31 female, 6 male) participating in the UK Veterans Family Study (UKVFS) explored the lived experiences of partners of military Veterans and their mental health/well-being. Data were analyzed using thematic analysis. Results: Three themes were created: military identity and culture, role-based identities, and loss of personal identity. Participants described ascribed and adopted identities including wife of, parent, employee, and member of the armed forces community. These identities interacted and changed but were underpinned by military life and culture both during and after service. Participants highlighted long-term impacts on self-esteem and confidence but also pride and resilience. Employment post-transition allowed restoration of personal identity but could come at the cost of belonging provided by the military community. Discussion: Findings demonstrate the challenges some military and Veteran partners experienced in maintaining personal identity and highlight both positive and negative long-term effects of military life on self-perceptions of identity. Many identities discussed were in service to others - often at the expense of partners' well-being and autonomy. Future research should concentrate on short- and long-term effects of identity loss/change on military and Veteran partners to inform and improve current and future strategies aiming to support partners.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".