Prevalence and risk factors of depression symptoms among rural and urban populations affected by Ebola virus disease in the Democratic Republic of the Congo: a representative cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: High mortality rates, anxiety and distress associated with Ebola virus disease (EVD) are risk factors for mood disorders in affected communities. This study aims to document the prevalence and risk factors associated with depressive symptoms among a representative sample of individuals affected by EVD. DESIGN: Cross-sectional study. SETTING: The current study was conducted 7 months (March 11, 2019 to April 23, 2019) after the end of the ninth outbreak of EVD in the province of Equateur in the Democratic Republic of the Congo (DRC). PARTICIPANTS: =34.05; SD=12.55) in health zones affected by the ninth outbreak in DRC. PRIMARY AND SECONDARY OUTCOME MEASURES: Participants completed questionnaires assessing EVD exposure level, stigmatisation related to EVD and depressive symptoms. The ORs associated with sociodemographic data, EVD exposure level and stigmatisation were analysed through logistic regressions. RESULTS: Overall, 62.03% (95% CI 59.66% to 64.40%) of individuals living in areas affected by EVD were categorised as having severe depressive symptoms. The multivariable logistic regression analyses showed that adults in the two higher score categories of exposure to EVD were at two times higher risk of developing severe depressive symptoms (respectively, OR 1.94 (95% CI 1.22 to 3.09); OR 2.34 (95% CI 1.26 to 4.34)). Individuals in the two higher categories of stigmatisation were two to four times more at risk (respectively, OR 2.42 (95% CI 1.53 to 3.83); OR 4.73 (95% CI 2.34 to 9.56)). Living in rural areas (OR 0.19 (95% CI 0.09 to 0.38)) and being unemployed (OR 0.68 (95% CI 0.50 to 0.93)) increased the likelihood of having severe depressive symptoms. CONCLUSIONS: Results indicate that depressive symptoms in EVD affected populations is a major public health problem that must be addressed through culturally adapted mental health programs.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".