Post-traumatic stress disorder and associated factors among soldiers retiring from active service in Uganda: Across sectional study
Bibliographic record
Abstract
Background Post-traumatic stress disorder (PTSD) is one of the commonest mental health challenges among veterans and service members. However, studies on PTSD and its associated factors among military personnel in Uganda are limited. This study estimated the prevalence of PTSD and associated factors among soldiers retiring from active service in Uganda. Methods In this cross- sectional study we recruited 247 retiring soldiers and assessed for PTSD using the PTSD check list for DSM-5. We also collected information on socio-demographic characteristics including gender, age, number of years in military service, level of education, and marital status, alcohol use, drug use, exposure to life, childhood trauma, and depression. participants Results Of the 247 participants, 97% (n=239) were male, 47% (n=115), the average age was 46 (SD=8.03) years, and the average duration of military service was 22 (SD= 8.36) years. The prevalence of PTSD among study participants was 13% (n=32). The factors associated with PTSD were moderate to hazardous alcohol consumption (aOR=3.44; 95% CI = 1.27 -9.28; p=0.02) and depression (aOR= 6.19; 95% CI = 2.15 – 17.84; p=0.0010). Conclusion This study found a 13% prevalence of PTSD among retiring military personnel in Uganda, with depression and hazardous alcohol use significantly increasing its odds. These findings underscore the need for targeted mental health screening and intervention during the transition to civilian life, particularly addressing depression and alcohol use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".