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Research and Development

Our Research and Development department ensures that every methodology that we create and deliver with our partners is rigorously tested, scalable and grounded in evidence - because meaningful impact for children requires proven solutions.

Driving evidence-based impact for children affected by conflict

Children growing up in conflict face complex challenges, from emotional distress and interrupted education to heightened protection risks. At the same time, the humanitarian sector is under increasing pressure to do more with less. 

Since 2016, War Child’s Research and Development Department has helped us design, test and improve our work. We work with academic institutions, partners and communities, combining research with practical experience to ensure our work delivers real results for children.

Our team of over 30 researchers, led by Professor Mark Jordans (University of Amsterdam and King’s College London), is one of the largest NGO research teams focused on children affected by conflict.
 
We publish our findings in peer-reviewed journals and share them across the humanitarian sector, helping build the global evidence base for mental health and psychosocial support, education in emergencies and child protection.

Director of Research and Development, Mark Jordans meeting with War Child partner Planning for Tomorrow (P4T) in Uganda.

From research to impact 

Our research underpins the War Child Care System, our toolkit of evidence-based methodologies that combine mental health and psychosocial support, education, and child protection to meet the integrated needs of children and communities affected by conflict.

To ensure these methodologies are effective, we follow a structured research process: from formative research and co-design with partners, to feasibility testing and rigorous evaluation.  

We use internationally recognised research methods, including randomised controlled trials, alongside implementation and qualitative research to understand why and how our methods are effective in different contexts.  

Our research journey

1. We develop through research

A research-grounded methodology is an approach that was developed based on formative research but has not yet been tested for effectiveness.

3. We test for effectiveness

An evidence-based methodology is an approach that has shown positive outcomes following at least one large-scale controlled effectiveness study.

2. We test for feasibility

An evidence-informed methodology is an approach that has shown promising outcomes following a feasibility study.

4. We implement at scale

We work with partners to bring and adapt evidence-based methodologies to more children and communities while maintaining quality and building on what the evidence tells us. 


A recognised leader in innovation

Our approach to combining research with practical experience has earned international recognition. 

2017 

War Child awarded the Klaus J. Jacobs Best Practice Prize for excellence in bridging science and practice.

2023

War Child awarded the Laureus Sport for Good Award for our TeamUp methodology

2024

War Child awarded the Yidan Prize for Education Development for Can’t Wait To Learn methodology.


Read our latest research and analysis

A community case detection tool to promote help-seeking for mental health care among children and adolescents in Ugandan refugee settlements: a stepped wedge cluster randomised trial

A community case detection tool to promote help-seeking for mental health care among children and adolescents in Ugandan refugee settlements: a stepped wedge cluster randomised trial

Strategies to promote mental health care help-seeking among children are needed, especially in lowincome and middle-income countries and in complex settings. The aim of this trial was to compare a vignette-based, community-level, proactive case detection tool (CCDT) against standard awareness raising for promoting mental health help-seeking among children and adolescents.

Optimising and personalising task-shared psychosocial interventions for common mental disorders: a Bayesian component network meta-analysis of individual participant data

Optimising and personalising task-shared psychosocial interventions for common mental disorders: a Bayesian component network meta-analysis of individual participant data

Although psychosocial interventions delivered by non-specialist providers effectively treat common mental disorders such as depression and anxiety, the treatment-specific elements driving their efficacy remain largely unknown. Our aim was to compare and rank the efficacy of the individual active components of task-shared psychosocial interventions and to predict efficacy using individual participant characteristics.