Bibliographic record
Abstract
I In nt te er rn na at ti io on na al l V Va ar ri ia at ti io on n i in n A AD DH HD D T Tr re ea at tm me en nt ts s Attention-deficit hyperactivity disorder (ADHD) has received wide international recognition as a chronic neurodevelopmental disorder leading to high levels of impairment.Cross-national variation in ADHD prevalence is now thought to be attributable to methodological differences in case finding rather than to cultural or national-level factors.However, as this month's lead article documents, treatment procedures for ADHD vary widely both across and within nations, and economic, historical, and political forces and cultural values play key roles.Stephen P. Hinshaw, Ph.D., and coauthors summarize data from a survey that addressed ADHD treatment policies and procedures in nine nations: Australia, Brazil, Canada, China, Germany, Israel, the Netherlands, Norway, and the United Kingdom.Representatives of these countries met in Berkeley, California, in March 2010 to discuss the survey findings and to develop ways to provide optimal services in light of the wide variation in policies and practices (page 459).C Co oe er rc ci io on n i in n P Ps sy yc ch hi ia at tr ri ic c T Tr re ea at tm me en nt t
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.106 | 0.047 |
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".