Developing mental health services during and in the aftermath of the Ebola virus disease outbreak in armed conflict settings: a scoping review
Bibliographic record
Abstract
BACKGROUND: Mental health is mostly affected by numerous socioeconomic factors that need to be addressed through comprehensive strategies. The aftermath of armed conflict and natural disasters such as Ebola disease virus (EVD) outbreaks is frequently associated with poor access to mental healthcare. To design the basis of improving mental health services via the integration of mental health into primary health care in the Democratic Republic of Congo (DRC), we conducted a scoping review of available literature regarding mental illness in armed conflict and EVD outbreak settings. METHODS: This scoping review of studies conducted in armed conflict and EVD outbreak of DRC settings synthesize the findings and suggestions related to improve the provision of mental health services. We sued the extension of Preferred Reporting Items for Systematic Reviews and Meta-Analyses to scoping studies. A mapping of evidence related to mental disorders in the eastern part of DRC from studies identified through searches of electronic databases (MEDLINE, Scopus, Psych Info, Google Scholar, and CINAHL). Screening and extraction of data were conducted by two reviewers independently. RESULTS: This review identified seven papers and described the findings in a narrative approach. It reveals that the burden of mental illness is consistent, although mental healthcare is not integrated into primary health care. Access to mental healthcare requires the involvement of affected communities in their problem-solving process. This review highlights the basis of the implementation of a comprehensive mental health care, through the application of mental health Gap Action Program (mhGAP) at community level. Lastly, it calls for further implementation research perspectives on the integration of mental healthcare into the health system of areas affecting by civil instability and natural disasters. CONCLUSION: This paper acknowledges poor implementation of community mental health services into primary health care in regions affected by armed conflict and natural disasters. All relevant stakeholders involved in the provision of mental health services should need to rethink to implementation of mhGAP into the emergency response against outbreaks and natural disasters.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".