Prevalence and risk factors of post-traumatic stress disorder and psychological distress symptoms in populations affected by Ebola in DR Congo before and during the COVID-19 pandemic
Bibliographic record
Abstract
Ebola virus disease (May 19–September 16, 2020) and COVID-19 simultaneously affected the province of Equateur in DR Congo (DRC). To date, no longitudinal studies have explored symptoms of post-traumatic stress disorder (PTSD) and psychological distress (PD) related to COVID-19 or Ebola in DRC. A representative sample of 1669 participants aged ≥18 was recruited in March–April 2019 (Time 1) and August–September 2020 (Time 2). Questionnaires assessed PTSD (PCL-5) and PD ( K −10) symptoms, social support, Ebola and COVID-19 exposure and related stigmatization. Prevalence of PTSD (58.24 % in T1 to 43.74% at T2, x 2 (1) = 5.83, p < .001) and PD symptoms (49.44 % in T1 to 28.94 % at T2, x 2 (1) = 5.83, p < .001) decreased from the Ebola outbreak to the COVID-19 pandemic. Populations living in rural areas consistently reported higher prevalence of PTSD and PD symptoms. Generalized estimating equation (GEE) models showed that stigmatization related to Ebola is the most important predictor of both PTSD ( B = 0.90, p < .0001) and PD ( B = 1.22, p < .001) symptoms, followed by exposure to Ebola ( B = 0.41, p < .001 and B = 0.56, p < .001). COVID-19 related stigmatization only predicted PTSD symptoms ( B = 0.21, p = .009). GEE models also confirmed that PTSD ( B = −0.78, p < .001) and PD ( B = −1.25, p < .001) decreased from Ebola outbreak to the COVID-19 pandemic. A significant interaction was found between Ebola stigmatization and time ( B = -0.40, p = .021) for PTSD, and between exposure to Ebola and time ( B = -0.36, p = .026) for PD. This study confirms that Ebola related stigmatization is the most important predictor of mental health problems. Community-based strategies can address, reduce, and eliminate this issue. • PTSD and psychological distress decreased from Ebola outbreak to COVID-19 pandemic. • Rural areas consistently reported higher PTSD and psychological distress symptoms. • Stigma related to Ebola is the most major predictor of PTSD and psychological distress. • Stigmatization related to COVID-19 was only associated with PTSD symptoms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".