Improving mental health and psychosocial wellbeing in humanitarian settings: reflections on research funded through R2HC
Bibliographic record
Abstract
Major knowledge gaps remain concerning the most effective ways to address mental health and psychosocial needs of populations affected by humanitarian crises. The Research for Health in Humanitarian Crisis (R2HC) program aims to strengthen humanitarian health practice and policy through research. As a significant portion of R2HC's research has focused on mental health and psychosocial support interventions, the program has been interested in strengthening a community of practice in this field. Following a meeting between grantees, we set out to provide an overview of the R2HC portfolio, and draw lessons learned. In this paper, we discuss the mental health and psychosocial support-focused research projects funded by R2HC; review the implications of initial findings from this research portfolio; and highlight four remaining knowledge gaps in this field. Between 2014 and 2019, R2HC funded 18 academic-practitioner partnerships focused on mental health and psychosocial support, comprising 38% of the overall portfolio (18 of 48 projects) at a value of approximately 7.2 million GBP. All projects have focused on evaluating the impact of interventions. In line with consensus-based recommendations to consider a wide range of mental health and psychosocial needs in humanitarian settings, research projects have evaluated diverse interventions. Findings so far have both challenged and confirmed widely-held assumptions about the effectiveness of mental health and psychosocial interventions in humanitarian settings. They point to the importance of building effective, sustained, and diverse partnerships between scholars, humanitarian practitioners, and funders, to ensure long-term program improvements and appropriate evidence-informed decision making. Further research needs to fill knowledge gaps regarding how to: scale-up interventions that have been found to be effective (e.g., questions related to integration across sectors, adaptation of interventions across different contexts, and optimal care systems); address neglected mental health conditions and populations (e.g., elderly, people with disabilities, sexual minorities, people with severe, pre-existing mental disorders); build on available local resources and supports (e.g., how to build on traditional, religious healing and community-wide social support practices); and ensure equity, quality, fidelity, and sustainability for interventions in real-world contexts (e.g., answering questions about how interventions from controlled studies can be transferred to more representative humanitarian contexts).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.184 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.007 | 0.031 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".