Bridging the Gap-establishing an Integrated Service for Adults with ADHD in India: The Amaha Model
Bibliographic record
Abstract
Purpose of Review: Attention-Deficit/Hyperactivity Disorder (ADHD) is increasingly recognised as a lifelong neurodevelopmental condition that persists into adulthood, often associated with significant functional impairment and high psychiatric comorbidity. Despite global advances, services in India remain severely underdeveloped. This paper reviews international guidelines—including those from NICE, the Canadian ADHD Resource Alliance (CADDRA), the British Association for Psychopharmacology (BAP), and the Indian Psychiatric Society (IPS)—to identify best practices for the assessment and multimodal treatment of adult ADHD. Collection and Analysis of Data: The paper reviews international guidelines on adult ADHD care, highlighting convergent recommendations and their applicability to the Indian context. It analyses systemic gaps in India’s service delivery, including the shortage of trained mental health professionals, low public and professional awareness, lack of culturally adapted tools and materials, and limited access to pharmacological and psychosocial interventions. As an applied example, the paper presents Amaha’s adult ADHD care model—developed in 2022—as a scalable, evidence-based approach tailored to the Indian ecosystem. The model integrates psychiatrist-led assessments using the DIVA-5 tool, structured psychoeducation, cautious pharmacotherapy, and adjunctive psychotherapy within a digitally enabled care framework prioritising, continuity of care, family engagement, and community support. Conclusions: This review proposes recommendations to strengthen ADHD care infrastructure in India. These include workforce training, public awareness initiatives, development of India-centric resources, telepsychiatry reforms, and integration of ADHD into public policy and insurance schemes. Addressing adult ADHD in India is both a clinical and public health imperative, requiring scalable, multidisciplinary, and context-sensitive solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".