Research Project 1
Research Project 1
批准号:
10672632
负责人:
Sisi Ma
金额:
$65.16万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-09-05 至 2028-08-31
关键词:
AddressAdministratorAdolescentAggressive behaviorCategoriesCharacteristicsChildChild Abuse and NeglectChild HealthChild WelfareClassificationClinicalClinical Decision Support SystemsCollaborationsCollectionComplexData ScienceData SetDedicationsDiseaseDoctor of PhilosophyEconomicsEducationEnsureEtiologyFamilyFamily memberGeneticGoalsHealthImpairmentIndividualInfrastructureInterventionKnowledgeMental DepressionMental HealthMethodsMinnesotaModelingObesityOutcomePerformancePopulationPositioning AttributePost-Traumatic Stress DisordersPreventionPrimary CarePublic Health InformaticsRecommendationRecording of previous eventsResearchResearch Project GrantsResourcesRiskRisk FactorsRisk ManagementRisk ReductionScientistSelf-Injurious BehaviorSeveritiesSexual abuseSignal TransductionSubstance abuse problemSystemTestingTimeTranslatingTranslationsUniversitiesWorkbehavioral healthcausal modelclinical translationdemographicsdesignfunctional disabilityimprovedindividualized preventioninsightmachine learning pipelinemachine learning predictionmaltreatmentmedical schoolsmembermodel buildingmultidisciplinaryneglectoutreachpediatricianpersonalized medicinepersonalized risk predictionphysical abusephysical conditioningpredictive modelingpreventpsychobiologicrisk predictionsocialsuccesssupport toolstooltranslational modeltraumatic stresstrustworthiness
中文摘要
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英文摘要
SUMMARY/ABSTRACT – PROJECT 1
Project 1 of the Center on Causal Data Science for Child and Adolescent Maltreatment Prevention
(CHAMP Center) will involve determining predictive and causal models for many categories of
maltreatment exposures and their consequential outcomes, and developing a scalable set of decision
support tools (DSTs) from these models. These DSTs will be designed to guide decisions by
practitioners in a position to prevent maltreatment and mitigate the severity of maltreatment-related
outcomes. Given that intervention on targets that are non-causal/non-etiological factors cannot
improve health or adaptive functioning, such decisions require that that the practitioner can
confidently: 1) identify etiological factors that cause health or functional impairments, and 2) target the
external or internal etiological factors relevant for these impairments for a particular child and family.
This becomes especially challenging when etiologies are complex and interventions must be
customized to precisely target diverse sets of causes, as is often true for children and families who
meet criteria for maltreatment-related interventions. The goal of Project 1 is to determine models and
to develop a set of DSTs to guide such decisions. We will do this by applying advanced causal data
science methods to several large, relevant data sets to derive valid causal models for several
categories of maltreatment exposure (sexual abuse, physical abuse, neglect) and maltreatment-
related outcomes (post-traumatic stress disorder, depression, self-harm, aggression, substance
abuse, obesity, functional impairment). We will integrate these analyses with machine learning
predictive classification to identify which predictive models can accurately classify children into risk
categories. We will work closely with members of the Resource Core to develop the models and of
the Dissemination & Outreach Core to engage the full spectrum of relevant stakeholders (domain
experts, primary care pediatricians, child welfare workers, behavioral health clinicians, agency
administrators, children, and family members) to ensure that the tools Project 1 develops are
effective, practical, trustworthy, and safe. One of the DSTs that Project 1 develops will form the basis
for Project 2, which will test this tool in a field trial. Insights from Project 2 will be essential for Project
1 to iteratively improve on the DSTs it develops.
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