Etats de stress en consultation externe à l’hôpital général de Bangui dans un contexte de conflit arme: étude transversale descriptive
Bibliographic record
Abstract
CONTEXT: in Africa's zones of conflict, recent studies report a high frequency of post-traumatic stress disorder (PTSD) particularly in community settings. OBJECTIVE: This study aimed to contribute to a better management of patients experiencing violence subsequent to the Central African Republic socio-political conflict. MATERIAL AND METHODS: We conducted a cross-sectional study of the medical records of patients receiving outpatient treatment in the Doctors Without Borders/Médecins Sans Frontières (France) Trauma Center, Bangui. RESULTS: 33.33% (n=35) of patients had PTSD, while 17.14% (n=18) of patients had acute stress syndrome. Stress syndrome (SS) was associated with female sex, rape, anxiety and depression. Rape multiplied the risk of SS occurrence by 8. The average age was 30 years (P25:22 years; P75:40 years). The majority of patients had mood disorder (63.81%; n=67). Insomnia was present in 62.83% (n=66) of patients. Hospital Anxiety and Depression Scale (HADS) was present in 44.76 % of patients. Depression was found in 40.95% (n=43) of patients. CONCLUSION: The obtained results show how the society, apart from militia members, is affected by conflict-related violence in the country. These results can enrich the reflections on health organisation and on the management of patients in Central African, by considering the impact of conflict-related acute stress syndome in the short, medium and long term.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".