Resilience of mental health services amidst Ebola disease outbreaks in Africa
Bibliographic record
Abstract
Introduction Health systems including mental health (MH) systems are resilient if they protect human life and produce better health outcomes for all during disease outbreaks or epidemics like Ebola disease and their aftermaths. We explored the resilience of MH services amidst Ebola disease outbreaks in Africa; specifically, to (i) describe the pre-, during-, and post-Ebola disease outbreak MH systems in African countries that have experienced Ebola disease outbreaks, (ii) determine the prevalence of three high burden MH disorders and how those prevalences interact with Ebola disease outbreaks, and, (iii) describe the resilience of MH systems in the context of these outbreaks. Methods This was a scoping review employing an adapted PRISMA statement. We conducted a five-step Boolean strategy with both free text and Medical Subject Headings (MeSH) to search 9 electronic databases and also searched WHO MINDbank and MH Atlas. Results The literature search yielded 1,230 publications. Twenty-five studies were included involving 13,449 participants. By 2023, 13 African nations had encountered a total of 35 Ebola outbreak events. None of these countries had a metric recorded in MH Atlas to assess the inclusion of MH in emergency plans. The three highest-burden outbreak-associated MH disorders under the MH and Psychosocial Support (MHPSS) framework were depression, post-traumatic stress disorder (PTSD), and anxiety with prevalence ranges of 1.4–7%, 2–90%, and 1.3–88%, respectively. Furthermore, our analysis revealed a concerning lack of resilience within the MH systems, as evidenced by the absence of pre-existing metrics to gauge MH preparedness in emergency plans. Additionally, none of the studies evaluated the resilience of MH services for individuals with pre-existing needs or examined potential post-outbreak degradation in core MH services. Discussion Our findings revealed an insufficiency of resilience, with no evaluation of services for individuals with pre-existing needs or post-outbreak degradation in core MH services. Strengthening MH resilience guided by evidence-based frameworks must be a priority to mitigate the long-term impacts of epidemics on mental well-being.
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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 | 0.014 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".