Gender differences in clinical presentation among treatment-seeking Veterans and Canadian Armed Forces personnel
Bibliographic record
Abstract
Introduction: Limited research has investigated gender differences among treatment-seeking Veterans and serving military personnel, despite important implications for treatment provision. In order to better serve the needs of women with military service, the authors sought to address this gap by examining the clinical presentation of men and women requesting services for military-related operational stress injuries (OSIs). Methods: Using a sample of 648 treatment-seeking male ( n = 550) and female ( n = 99) Veterans and Canadian Armed Forces (CAF) personnel, the authors compared prevalence of childhood sexual and physical abuse, probable mental health diagnoses (posttraumatic stress disorder [PTSD], depression, and generalized anxiety disorder [GAD]), and severity of pain and somatic symptoms. Results were rerun to control for sociodemographic variables that significantly differed by gender. Results: Rates of probable PTSD were higher for women ( p < 0.05), and women reported significantly more somatic symptoms ( p < 0.001), pain severity ( p < 0.01), and childhood sexual abuse (47% of the sample; p < 0.001). Both men and women reported equally high rates of childhood physical abuse (71% for both genders). Discussion: Women in this study had a higher prevalence of probable PTSD and childhood sexual abuse, and reported higher severity of pain and somatic symptoms. The study highlights the diverse range of issues that are clinically relevant for – and may complicate the treatment of – women with military service who have OSIs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".