Practitioner Review: It's time to bridge the gap – understanding the unmet needs of consumers with attention‐deficit/hyperactivity disorder – a systematic review and recommendations
Bibliographic record
Abstract
OBJECTIVE: Understanding the unmet needs of healthcare consumers with attention-deficit/hyperactivity disorder (ADHD) (individuals with ADHD and their caregivers) provides critical insight into gaps in services, education and research that require focus and funding to improve outcomes. This review examines the unmet needs of ADHD consumers from a consumer perspective. METHODS: A standardised search protocol identified peer-reviewed studies published between December 2011 and December 2021 focusing on consumer-identified needs relating to ADHD clinical care or research priorities. RESULTS: 1,624 articles were screened with 23 studies that reviewed examining the needs of ADHD consumers from Europe, the U.K., Hong Kong, Iran, Australia, the U.S.A. and Canada. Consumer-identified needs related to: treatment that goes beyond medication (12 studies); improved ADHD-related education/training (17 studies); improved access to clinical services, carer support and financial assistance (14 studies); school accommodations/support (6 studies); and ongoing treatment efficacy research (1 study). CONCLUSION: ADHD consumers have substantial unmet needs in clinical, psychosocial and research contexts. Recommendations to address these needs include: improving access to and quality of multimodal care provision; incorporating recovery principles into care provision; fostering ADHD health literacy; and increasing consumer participation in research, service development and ADHD-related training/education.
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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 | 0.055 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".