Posttraumatic Stress Disorder, Depression, and HIV Risk Behavior Among Ohio Army National Guard Soldiers
Bibliographic record
Abstract
We examined the relationship between posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and human immunodeficiency virus (HIV) risk behavior among the Ohio Army National Guard (OHARNG). We analyzed data collected from a sample of OHARNG enlisted between June 2008 and February 2009. Participants completed interviews assessing HIV risk activities defined by the Behavioral Risk Factor Surveillance System, and were screened for PTSD and MDD based on DSM-IV criteria according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM-IV; American Psychiatric Association, 1994). Logistic regression was used to examine the independent and combined effects of PTSD and MDD on past-year HIV risk behavior. Of 2,259 participants, 142 (6.3%) reported at least 1 past-year HIV risk behavior. In adjusted models, relative to soldiers with neither disorder, screening positive for MDD only was associated with HIV risk behavior (adjusted odds ratio [AOR] = 2.33, 95% CI = [1.15, 4.71]), whereas PTSD was not significant (AOR = 1.60, 95% CI = [0.80, 3.20]). Participants with both PTSD and depression were most likely to report HIV risk behavior (AOR = 2.75, 95% CI = [1.06, 7.11]). Soldiers with PTSD and MDD may be at greater risk for HIV infection due to increased engagement in HIV risk behavior. Integrated interventions to address mental health problems and reduce HIV risk behavior are in need of development and evaluation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".