Integration of psychological interventions in multi-sectoral humanitarian programmes: a systematic review
Bibliographic record
Abstract
BACKGROUND: Every year, millions of people are affected by humanitarian crises. With a growing population of people affected, the need for coordination and integration of services aiming to improve the effectiveness of mental health and psychosocial support also grows. In this study, we examine how psychological interventions in humanitarian settings globally have been implemented through integration into programming outside of formal healthcare delivery through multisectoral integration. METHODS: A comprehensive search of six databases and reference checking was undertaken in 2022. We included studies focusing on implementation strategies and implementation outcomes of multi-sectoral, integrated psychological interventions, with no year limits. We extracted data using the software Covidence, and used the software to manage screening and reviewing processes. All studies were critically appraised for quality and rigor using the mixed-methods appraisal tool. RESULTS: Eight studies were included in total. We found that interventions targeted conflict affected, displaced and disaster recovering populations. The interventions demonstrated moderate success in reducing psychological distress and enhancing disaster preparedness. We found that key implementation outcomes investigated and prioritised include acceptability, feasibility, and relevance. The studies reported on integration processes that involved task shifting primarily, with an emphasis on different formats of adaptation, partnership creation and capacity development to maximise effectiveness of integrated interventions. CONCLUSION: Overall, there is little research being done to rigorously document the processes and experiences of integrating psychological interventions with non-health interventions. This could be an indication that, while multisectoral integration may be more common in practice, little research is being done or reported in this area formally. There is an urgent need for further research into integrated multi-sectoral interventions. This research should aim to understand how social, cultural, and environmental contexts in different ways, and to different degrees, affect what is acceptable and feasible to deliver and how these ultimately influence the impact of integrated interventions.
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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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".