SIMILARITIES IN SPECIFIC PHYSICAL HEALTH DISORDER PREVALENCE AMONG FORMERLY DEPLOYED CANADIAN FORCES VETERANS WITH FULL AND SUBSYNDROMAL PTSD
Bibliographic record
Abstract
BACKGROUND: The link between posttraumatic stress disorder (PTSD) and deleterious physical health consequences among previously deployed military veterans has been well documented. Research has focused primarily on investigating prevalence rates of physical health disorders among individuals with PTSD. Far less research has compared prevalence rates of specific physical health disorders among individuals with full and subsyndromal PTSD. The current study investigated differences in the prevalence of seven specific categories of physical health disorders (i.e. musculoskeletal, circulatory, endocrine, respiratory, gastrointestinal, neurological, and other physical health disorders) among individuals with full PTSD, subsyndromal PTSD, and no PTSD (i.e. controls). METHODS: Participants were from a sample of Canadian Forces Veteran's Affairs clients (n = 990; 96.7% men) who were previously deployed to an overseas combat theatre. RESULTS: Logistic regressions indicated four categories of physical health conditions (musculoskeletal, neurological, gastrointestinal, and other physical health disorders) were more likely to be present among those with full PTSD compared to those in the control group. Further, five physical health disorder categories (musculoskeletal, neurological, respiratory, gastrointestinal, and other physical health disorders) were more likely to be present among those with subsyndromal PTSD when compared to those in the control group. There were no observed significant differences between full and subsyndromal PTSD. CONCLUSIONS: Current results suggest similar patterns of specific physical health disorder prevalence among those with full and subsyndromal PTSD, which differ consistently from patterns of specific physical health disorders among those in the control group. Comprehensive results, implications, and directions for future research will be discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".