Objective
Children affected by humanitarian crises face conflict-related violence, human rights abuses, extreme poverty, and natural disasters. Exposure to adversity increases the risk of psychological distress and posttraumatic stress disorder (PTSD). Controversial and inconsistent evidence on the effectiveness of mental health and psychosocial support interventions (MHPSS-I) for children exposed to humanitarian crises suggests that some interventions may be harmful, impede recovery and increase psychological symptoms. Novel research is warranted also because current evidence averages response at the group level, obscuring individual differences. Against this background, I propose to develop a precision psychological care algorithm that - considering individual clinical and socio-demographic data - will reliably predict child response to MHPSS-I. PEACE is a multi-phase ambitious research project. I will start with the application of network meta-analytic techniques using individual participant data, for developing prediction models of interventions’ response accounting for person-level differences. Prediction models will be complemented by mediation analyses to shed light on the mechanisms of action of MHPSS-I. Then, the individual participant dataset will be transferred into a machine learning algorithm, able to predict how, under what set of circumstances, and for which child a specific MHPSS-I is effective in reducing symptoms of PTSD, and improving functioning and wellbeing. Next, the algorithm will be tested in an enrichment-design randomized controlled trial, conceived to test precision care models and never applied before in mental health. PEACE will open a new era for the delivery of MHPSS-I to traumatized children, abandoning the “one-size-fits-all” approach, maximizing potential benefits, and avoiding waste of resources and unnecessary risk exposure for vulnerable children.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- medical and health sciences clinical medicine psychiatry posttraumatic stress disorder
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-COG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
37129 Verona
Italy
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.