Burden of post-traumatic stress disorder acute exacerbations during the commemorations of the genocide against Tutsis in Rwanda: a cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Following the 1994 genocide against Tutsis in Rwanda, the prevalence of post-traumatic stress disorder (PTSD) is high. In a period of seven days every year in April, Rwandans gather to mourn the victims of the genocide. During this commemoration period, survivors living with chronic PTSD experience PTSD acute exacerbations (PAE). We assessed factors associated with severe PAE during the annual commemoration period of the genocide against Tutsis in Rwanda. METHODS: We carried out a retrospective cross-sectional study that included people who had PAE during the commemoration week in April 2011 across Huye District in Rwanda. Our outcome measure was PAE categorized into three levels: < 15 minutes, 15-30 minutes, and > 30 minutes. Ordinal logistic regression analyses were performed to identify factors associated with severe PAE. RESULTS: We enrolled 383 people with PAE, of whom 71.8% were female and 53.5% were aged 20-45 years. All participants reported history of PAE, of which 59.8% had experienced more than two PAE during the previous commemoration periods. 33.2% had PAE that lasted > 30 minutes. History of PAE (> twice) (OR = 1.86; 95% CI = 1.27-2.75) and having lost a partner in genocide (OR = 2.19; 95% CI = 1.01-4.81) were associated with severe PAE, after adjusting for sex and age. CONCLUSION: Our findings suggest that PAE is frequent during the commemoration periods. People who reported having more prior PAE and being widow (er) were more likely to have severe PAE. While history of PAE and bereavement status are non-modifiable factors, our findings could help identify and target these people who are at risk for severe PAE.
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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 | 0.001 |
| 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 | 0.001 |
| 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".