Prevalence and determinants of anxiety, depression and comorbid anxiety–depression symptoms among adolescents in Ebola-affected zones
Bibliographic record
Abstract
Background Ebola virus disease (EVD) has been shown to be associated with poor mental health in affected zones. However, no study has yet explored its impact on adolescents’ mental health. Aims This study aimed to assess the prevalence and risk factors associated with depression and anxiety symptoms among adolescents in EVD-affected areas in the Equateur Province of the Democratic Republic of the Congo during the COVID-19 pandemic. Method A provincial sample of adolescents aged 12–17 years (M = 14.84, s.d. = 1.49) living in the 18 urban and rural areas affected by the 2018 EVD outbreak completed a two-wave longitudinal survey. Surveys assessed symptoms of depression and anxiety, exposure to Ebola, social support and sociodemographic information. Results A total of 490 participants completed the baseline and follow-up surveys, 50% of whom were female. Elevated and worsened depressive symptoms were observed among participants from the baseline (56.94%) to the follow-up (91.43%; z = −11.37, P < 0.001), whereas anxiety symptoms decreased from the baseline (36.33%) to follow-up (24.90%; z = 4.06, P < 0.001). The final generalised estimating equation model showed that anxiety symptoms decreased over time (B = −3.92, P < 0.001), while depression symptoms increased (B = 4.79, P < 0.001). Stigmatisation related to Ebola positively predicted anxiety (B = 5.41, P < 0.001) and depression symptoms (B = 0.4452, P = 0.009). Social support negatively predicted anxiety (B = −1.13, P = 0.004) and depression (B = −0.98, P < 0.001) symptoms but only moderated the association between stigmatisation and depression symptoms (B = −0.67, P < 0.001). Conclusions Most adolescents living in EVD-affected areas experience mental health issues. Stigmatisation related to EVD and living in urban areas are the most consistent predictors of mental health problems. Nevertheless, social support remains a protective factor for depression and anxiety symptoms and a necessary resource for building resilience.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".