Do we over‐diagnose ADHD in North America? A critical review and clinical recommendations
Bibliographic record
Abstract
There has been a marked increase in the prevalence of attention-deficit/hyperactivity disorder (ADHD) in the last 25 years in North America. Some see this trend as positive and believe that it reflects a better identification of ADHD and even think that the disorder is still under-diagnosed. Others, however, contend that ADHD is over-diagnosed. To help mental health clinicians to maintain an informed and nuanced perspective on this debate, this critical review aims to (1) summarize empirical results on factors that might contribute to increase the number of ADHD diagnoses and (2) propose clinical recommendations coherent with these findings to improve clinical practices for ADHD assessment and treatment. We conclude that artifactual factors such as current formulation of diagnostic criteria, clinical practices, and inordinate focus on performance, which is rampant in North America, likely contribute to inflated prevalence rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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 teacher head, 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".