Health Care Utilization by United Nations Peacekeeping Veterans with Co-occurring, Self-Reported, Post-Traumatic Stress Disorder and Depression Symptoms versus Those Without
Bibliographic record
Abstract
It remains to be determined whether patients with comorbid post-traumatic stress disorder (PTSD) and depression use more health care resources than do those without. United Nations peacekeeping veterans from Canada were divided into four groups, i.e., PTSD alone (n = 23), depression alone (n = 167), comorbid PTSD and depression (n = 119), and neither (n = 164), and compared with respect to total number of visits to any health care professional in the past year. Analysis of variance revealed that the groups significantly differed in total visits. Post hoc analyses indicated that veterans with co-occurring PTSD and depression symptoms had more visits than did those in the other groups and that veterans with PTSD symptoms alone and depression symptoms alone had more visits than did those with neither PTSD nor depression. Additional analyses revealed that veterans with co-occurring PTSD and depression symptoms made more visits to general practitioners, specialists, pharmacists, and mental health professionals than did the others. Future research directions and implications for treatment planning are 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".