Association of PTSD and Depression With Medical and Specialist Care Utilization in Modern Peacekeeping Veterans in Canada With Health-Related Disabilities
Bibliographic record
Abstract
OBJECTIVE: We examined the relative associations between posttraumatic stress disorder (PTSD) and depression severity with medical and specialist care use in modern peacekeeping veterans with health-related disabilities. METHOD: The participants consisted of 1016 male veterans who served in the Canadian Forces from 1990 to 1999, selected from a larger random sample of 1968 veterans who voluntarily completed an anonymous general health survey conducted by Veterans Affairs Canada in 1999. Survey instruments included the PTSD Checklist-Military Version (PCL-M), Center for Epidemiological Studies-Depression Scale, and questionnaires of health problems and service use, sociodemographic characteristics, and military history. RESULTS: Among peacekeeping veterans with health disabilities, "probable" PTSD (PCL-M score > or = 50) was associated with significantly more medical service use (primary and specialty care combined), with a mean of 16.4 times (SD = 17.4) compared with 6.0 times (SD = 6.6), p < .001, for veterans without PTSD. We found that in multivariate analyses, general medical care intensity (i.e., number of visits) was related to increased health problems, greater probable PTSD diagnosis, and greater depression symptom severity. We also found that depression severity accounted for health care use intensity and that PTSD only added a small amount of incremental variance above that. CONCLUSIONS: The observed association between PTSD (diagnosis and severity) and medical care utilization stresses the importance of PTSD screening in primary care settings, especially in patients with a history of military service. This association is also useful for clinicians and hospital administrators in understanding potential medical and psychiatric needs for military veterans.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".