What’s Wrong with Me? What’s Wrong with You? The Issue of Over-Diagnosing ADHD in Children
Bibliographic record
Abstract
Historically, the field of mental health has been shrouded in controversy and conflict. The problems associated with diagnosing mental illnesses are still prevalent today, and this process becomes even more complicated when assessing children, who have yet to develop mature social skills and cognitive functioning. Attention deficit hyperactivity disorder (ADHD) is one of the mental health conditions that is diagnosed using the Diagnostic and Statistical Manual of Mental Disorders (DSM). Overwhelming support from the primary literature suggests that the current procedures of diagnosing ADHD- which begin during childhood- allow for a high degree of subjectivity, inconsistency, and uncertainty. For these reasons, the issue of over-diagnosing ADHD in children has become more significant, and more plausible than ever before. By outlining the key factors that contribute to this problem, certain modifications can be made to improve the ADHD diagnostic procedures for future applications. These changes can increase the accuracy of mental health assessments, thus minimizing the number of false positive diagnoses of ADHD in children worldwide.
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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 | 0.022 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".