Threatened Health in Women: A Qualitative Study on the Wives of War Veterans with Post-Traumatic Stress
Bibliographic record
Abstract
INTRODUCTION & AIM: Post-traumatic stress disorder causes distress and dysfunction in the life of the wives of veterans, which causes physical and mental health problems with the continuation of life. This study examined the life experiences of wives of war veterans with post-traumatic stress.MATERIALS & METHODS: This qualitative study using qualitative content analysis with the participation of 16 wives of war veterans with post-traumatic stress in Golestan province in Iran was conducted in 2015. Data was collected through semi-structured interviews and by purposive sampling and continued until data saturation. Data analysis was done continuously and simultaneously with data collection by content analysis method.FINDINGS: Four main categories and nine sub-categories including mental health (mental health problems and the memories), physical function (physical injuries and sleep disorders), captivity in life (humiliation, lack of independence in life), isolation (impairment in social interaction), dysfunction life (damage to the sons, the defect in family interactions) were the main findings of this study, which causes health threats.CONCLUSION: Spouses of veterans have many problems in their daily lives and caregivers by understanding their needs and enhancing information systems, and social support can improve the function of their 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".