Prevalence and correlates of probable post-traumatic stress disorder and common mental disorders in a population with a high prevalence of HIV in Zimbabwe
Bibliographic record
Abstract
Background: We investigated the prevalence of and factors associated with post-traumatic stress disorder (PTSD) and common mental disorders (CMDs), which include depression and anxiety disorders, in a setting with a prevalence of high human immunodeficiency virus (HIV) within a primary care clinic, using the PTSD Checklist for DSM-5 and the 14-item Shona Symptom Questionnaire, both locally validated screening tools.Methods: A cross-sectional survey was carried out with adult patients (n = 204) from the largest primary care clinic facility in Harare, Zimbabwe, in June 2016.Results: A total of 83 patients (40.7%) met the criteria for probable PTSD, of whom 57 (69.5%) had comorbid CMDs. Among people living with HIV, 42 (55.3%) had PTSD. Probable PTSD was associated with having experienced a negative life event in the past 6 months [adjusted odds ratio (OR) 3.73, 95% confidence interval (CI) 1.49–9.34] or screening positive for one or more CMD (adjusted OR 6.48, 95% CI 3.35–2.54).Conclusion: People living with HIV showed a high prevalence of PTSD and CMD comorbidity. PTSD screening should be considered when the CMD screen is positive and there is a history of negative life events.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".