Leveraging AI to Investigate Child Maltreatment Text Narratives: Promising Benefits and Addressable Risks
AbstractThe trove of information contained in child maltreatment narratives represents an opportunity to strengthen the evidence base for policy reform in this area, yet it remains underutilized by researchers and policy makers. Current research into child maltreatment often involves the...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2025-07-01
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| Series: | JMIR Pediatrics and Parenting |
| Online Access: | https://pediatrics.jmir.org/2025/1/e73579 |
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| _version_ | 1849246210578186240 |
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| author | Wilson Lukmanjaya Tony Butler Sarah Cox Oscar Perez-Concha Leah Bromfield George Karystianis |
| author_facet | Wilson Lukmanjaya Tony Butler Sarah Cox Oscar Perez-Concha Leah Bromfield George Karystianis |
| author_sort | Wilson Lukmanjaya |
| collection | DOAJ |
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AbstractThe trove of information contained in child maltreatment narratives represents an opportunity to strengthen the evidence base for policy reform in this area, yet it remains underutilized by researchers and policy makers. Current research into child maltreatment often involves the use of qualitative methodologies or structured survey data that are either too broad or not representative, thereby limiting the development of effective policy responses and intervention strategies. Artificial intelligence (AI) approaches such as large language models (AI models that understand and generate language) can analyze large volumes of child maltreatment narratives by extracting population-level insights on factors of interest such as mental health and treatment needs. However, when applying such methods, it is useful to have a framework on which to base approaches to the data. We propose a seven step framework: (1) data governance; (2) researcher vetting; (3) data deidentification; (4) data access; (5) feasibility testing of baseline methods; (6) large-scale implementation of black box algorithms; and (7) domain expert result validation for such exercises to ensure careful execution and limit the risk of privacy and security breaches, bias, and unreliable conclusions. |
| format | Article |
| id | doaj-art-e4d1b9d9431c402d97dc6db7b580aaae |
| institution | Kabale University |
| issn | 2561-6722 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Pediatrics and Parenting |
| spelling | doaj-art-e4d1b9d9431c402d97dc6db7b580aaae2025-08-20T03:58:35ZengJMIR PublicationsJMIR Pediatrics and Parenting2561-67222025-07-018e73579e7357910.2196/73579Leveraging AI to Investigate Child Maltreatment Text Narratives: Promising Benefits and Addressable RisksWilson Lukmanjayahttp://orcid.org/0000-0002-7747-4648Tony Butlerhttp://orcid.org/0000-0002-2679-2769Sarah Coxhttp://orcid.org/0000-0003-0670-0692Oscar Perez-Conchahttp://orcid.org/0000-0002-8823-7090Leah Bromfieldhttp://orcid.org/0000-0002-4263-5878George Karystianishttp://orcid.org/0000-0003-3491-361X AbstractThe trove of information contained in child maltreatment narratives represents an opportunity to strengthen the evidence base for policy reform in this area, yet it remains underutilized by researchers and policy makers. Current research into child maltreatment often involves the use of qualitative methodologies or structured survey data that are either too broad or not representative, thereby limiting the development of effective policy responses and intervention strategies. Artificial intelligence (AI) approaches such as large language models (AI models that understand and generate language) can analyze large volumes of child maltreatment narratives by extracting population-level insights on factors of interest such as mental health and treatment needs. However, when applying such methods, it is useful to have a framework on which to base approaches to the data. We propose a seven step framework: (1) data governance; (2) researcher vetting; (3) data deidentification; (4) data access; (5) feasibility testing of baseline methods; (6) large-scale implementation of black box algorithms; and (7) domain expert result validation for such exercises to ensure careful execution and limit the risk of privacy and security breaches, bias, and unreliable conclusions.https://pediatrics.jmir.org/2025/1/e73579 |
| spellingShingle | Wilson Lukmanjaya Tony Butler Sarah Cox Oscar Perez-Concha Leah Bromfield George Karystianis Leveraging AI to Investigate Child Maltreatment Text Narratives: Promising Benefits and Addressable Risks JMIR Pediatrics and Parenting |
| title | Leveraging AI to Investigate Child Maltreatment Text Narratives: Promising Benefits and Addressable Risks |
| title_full | Leveraging AI to Investigate Child Maltreatment Text Narratives: Promising Benefits and Addressable Risks |
| title_fullStr | Leveraging AI to Investigate Child Maltreatment Text Narratives: Promising Benefits and Addressable Risks |
| title_full_unstemmed | Leveraging AI to Investigate Child Maltreatment Text Narratives: Promising Benefits and Addressable Risks |
| title_short | Leveraging AI to Investigate Child Maltreatment Text Narratives: Promising Benefits and Addressable Risks |
| title_sort | leveraging ai to investigate child maltreatment text narratives promising benefits and addressable risks |
| url | https://pediatrics.jmir.org/2025/1/e73579 |
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