Assessment of prevalence and determinants of anxiety and psychological distress symptoms in Ebola child and adolescent survivors and orphans in Eastern Democratic Republic of the Congo during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: To date, only three studies investigated the mental health of youth affected by Ebola virus disease (EVD). None explored anxiety and psychological distress symptoms in survivors or orphans. This study aimed to investigate the prevalence and determinants of anxiety and psychological distress symptoms among survivors and orphans of the 2018-2020 Ebola epidemic in Eastern Democratic Republic of the Congo (DRC) during the COVID-19 pandemic. METHODS: A representative sample of 416 participants (mean age = 13.37, SD = 2.79, 51.20 % girls, 146 survivors, 233 orphans, and 34 orphan-survivor participants) completed measures evaluating anxiety, psychological distress, exposure, resilience, stigmatization related to Ebola and COVID-19. RESULTS: = 113.50, p < .001. Ebola and COVID-19 related stigmatization were the most important determinants of anxiety (B = 0.40, p < .001; B = 0.37, p < .001) and psychological distress (B = 0.48, p < .001; B = 0.44, p < .001). Resilience was negatively associated with both anxiety and psychological distress. The final regression models explained 49 % and 85 % of the variance of anxiety and psychological distress. LIMITATIONS: The cross-sectional design used prevents to establish causal link. CONCLUSIONS: Ebola children and adolescents' survivors and orphans are at major risk of experiencing anxiety and psychological distress in Eastern RDC affected by years of armed conflict. Massive resources are needed to develop and implement programs to reduce stigma and support mental health.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".