Paging Dr. Google: Availability and Reliability of Online Evidence-Based Treatment Information about ADHD
Bibliographic record
Abstract
It is becoming increasingly common for caregivers and patients to search for health and mental health information on the Internet. Although there is a sizable scientific literature outlining an evidence-based approach to managing Attention Deficit Hyperactivity Disorder (ADHD) in children and adolescents, it is not clear how much of the online information about the disorder and its treatment aligns with evidence-based practice. The goal of this study was to conduct a review of online information about ADHD treatment and to systematically analyze this information with respect to accountability, presentation, content and alignment with evidence-based practice, and readability. Thirty-one ADHD-themed websites identified by three common Internet search engines were coded using a set of standardized criteria. Results indicated that the quality of information about ADHD treatment was generally poor, with websites meeting less than half the standardized criteria. Alignment with evidence-based practice was especially poor; most websites did not discuss psychosocial treatments and very few mentioned the treatment guidelines produced by the American Academy of Pediatrics. Flesch-Kincaid reading level was, on average, much higher than the recommended grade 8 level. Results indicate that, although conducting online searches about ADHD treatment could be beneficial in the context of shared-decision making, it is important for clinicians and caregivers to understand the limitations of this approach and to continue to engage in evidence-based treatment of ADHD to ensure positive outcomes for children and adolescents with the disorder.
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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 | 0.310 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".