Relationship among combat experience, Veteran pathology, and pathology of Veterans’ intimate partners: Factors predicting the pathology of Veterans and their intimate partners
Bibliographic record
Abstract
LAY SUMMARY For nearly 20 years, military members and their families have been involved in some form of military operation in support of what is known as the Global War on Terrorism. Research has shown that military members and Veterans demonstrate increased levels of mental health disorders, such as anxiety, depression, and posttraumatic stress disorder. No studies to date, however, have explored how the resulting mental illness is shared by the intimate partners of these military members and Veterans. For this research, the term “ resonating of pathology” is used to identify this phenomenon. The research authors surveyed combat Veterans and their intimate partners to gather the data for analysis. The authors then completed statistical analysis to examine both associations and predictive factors that would help clinicians, researchers, and academics understand and develop theories and clinical interventions for such couples. Although the research appears to confirm this sharing of mental health diagnosis, more research will be needed to create a better understanding in the future.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".