Integrating Mental Health and Psycho-Social Support (MHPSS) into infectious disease outbreak and epidemic response: an umbrella review and operational framework
Bibliographic record
Abstract
Abstract Introduction Infectious disease outbreaks have a substantial impact on people’s psychosocial well-being. Yet, mental health and psychosocial support (MHPSS) interventions are not systemically integrated into outbreak and epidemic response. Our review aims to synthesise evidence on the effectiveness of MHPSS interventions in outbreaks and propose a framework for systematically integrating MHPSS into outbreak response. Methods We conducted an umbrella review in accordance with the Joanna Briggs Institute (JBI) methodology for umbrella reviews. Results We identified 23 systematic literature reviews, 6 of which involved meta-analysis, and only 30% (n=7) were of high quality. Most of the available literature was produced during COVID-19 and focused on clinical case management and medical staff well- being, with scarce evidence on the well-being of other outbreak responders and MHPSS in other outbreak response pillars. Conclusion Despite the low quality of the majority of the existing evidence, MHPSS interventions have the potential to improve the psychological well- being of those affected by and those responding to outbreaks. They also can improve the outcomes of the outbreak response activities such as contact tracing, infection prevention and control, and clinical case management. Our proposed framework would facilitate integrating MHPSS into outbreak response and hence mitigate the mental health impact of outbreaks. Review registration PROSPERO CRD42022297138.
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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 | 0.082 | 0.181 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.036 | 0.023 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".