Assessing Unaddressed Information Needs in Attention-Deficit/Hyperactivity Disorder: A Mixed Methods Study Utilizing Large Language Model–Enhanced Semantic Analysis of Online Community Discussions
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By
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Jaeeun Baek
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Hyeoneui Kim
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September 11, 2026
Clinical Report: Assessing Unaddressed Information Needs in ADHD
Overview
This study highlights significant unmet information needs among individuals with ADHD and their caregivers, despite the availability of extensive online resources.
Background
ADHD is a prevalent neurodevelopmental disorder affecting children and adolescents, with behavioral disorders representing a significant portion of mental health issues in this demographic. Many caregivers report dissatisfaction and unmet needs regarding ADHD management.
Data Highlights
No numerical or trial data presented in the source material.
Key Findings
- 46.7% of reported mental health issues among children and adolescents are behavioral disorders, including ADHD.
- 92% of the most-viewed TikTok videos on ADHD tests contained insufficient information.
- Parents' inadequate knowledge of ADHD may lead to maladaptive parenting practices.
- Health care providers' information priorities may not align with those of patients and caregivers.
- Timely access to reliable information is essential for effective ADHD management.
Clinical Implications
Healthcare providers should recognize the discrepancies between the information needs of caregivers and the resources available.
Conclusion
Ensuring access to reliable information is vital for effective treatment.
Related Resources & Content
- Sikirica V, Flood E, Dietrich CN, et al., Patient, 2015 -- Unmet needs associated with attention-deficit/hyperactivity disorder in eight European countries as reported by caregivers and adolescents: results from qualitative research
- Nurhidayah I, Nurhaeni N, Hanifah I, Allenidekania A, JPK, 2024 -- Information needs among parents of cancer children: a systematic review
- Scarpellini F, Bonati M, Child, 2023 -- Transition care for adolescents and young adults with attention‐deficit hyperactivity disorder (ADHD): a descriptive summary of qualitative evidence
- Claussen AH, Holbrook JR, Hutchins HJ, et al., Prev Sci, 2024 -- All in the family? A systematic review and meta-analysis of parenting and family environment as risk factors for attention-deficit/hyperactivity disorder (ADHD) in children
- Faller H, Koch U, Brähler E, et al., J Cancer Surviv, 2016 -- Satisfaction with information and unmet information needs in men and women with cancer
- Journal of Medical Internet Research (JMIR) — Large Language Model–Assisted Annotation Framework for Cross-Platform Analysis of Online Autism Communities: Implications for Parent Education and Digital Support
- npj Digital Medicine — Utilizing Large Language Models to Enhance Diagnosis of Language Disorders Linked to Autism and Recognize Unique Characteristics
- JAMA Network Open — Problematic Social Media Use and Attention-Deficit/Hyperactivity Disorder Symptoms in Adolescents
- BMC Psychiatry (Springer) — Characterizing Symptoms of SpLD, ADHD, and ASD in Chinese Children: A Biopsychosocial and Transdiagnostic Approach
- NICE ADHD Guideline
- Large Language Model–Assisted Annotation Framework for Cross-Platform Analysis of Online Autism Communities
- Utilizing Large Language Models to Enhance Diagnosis of Language Disorders Linked to Autism
- Problematic Social Media Use and Attention-Deficit/Hyperactivity Disorder Symptoms in Adolescents
- Comparative efficacy and acceptability of pharmacological, psychological, and neurostimulatory interventions for ADHD in adults: a systematic review and component network meta-analysis - The Lancet Psychiatry
- Consensus Statement on Digital Health and Attention-Deficit/Hyperactivity Disorder by the European Network for ADHD (EUNETHYDIS): Modified Delphi Study - PMC
Based on findings from:
What Provider Frequently Asked Questions Miss: Evaluating Unmet Attention-Deficit/Hyperactivity Disorder Information Needs Through Comparison of Online Community Posts Using Large Language Model–Assisted Semantic Analysis in a Mixed Methods Study
Jaeeun Baek, Hyeoneui Kim. Journal Of Medical Internet Research, 2026.
https://www.jmir.org/2026/1/e96060
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.