A Survey of the Quality of Web Based Information on the Treatment of Schizophrenia and Attention Deficit Hyperactivity Disorder
Bibliographic record
Abstract
OBJECTIVE: To systematically assess the quality, accountability and readability of Internet information on the treatment of schizophrenia and Attention Deficit Hyperactivity Disorder (ADHD), using a standardized pro forma. METHOD: We analysed the 20 most highly ranked pages on the treatment of ADHD and schizophrenia, identified by five common Internet search engines. RESULTS: There was little overlap in the sites identified by different search engines. In the case of schizophrenia, one site was identified three times and another eight sites twice; while for ADHD four sites were identified twice. Accountability (Silberg score), presentation and readability, as assessed by the Flesch-Kincaid Grade Level score, were poor. Mean Silberg, presentation and Flesch-Kincaid Grade Level scores were 3.2 (range 0-9) out of 9, 1.9 (range 0-4) out of 4, and 11.5 (range 6.5-12.25), respectively. There was no statistical difference in scores between the two diagnoses. Depending on the recommendation, agreement with evidence-based practice for schizophrenia ranged from only 2 to 55% (mean = 2.8 (range 0-9) out of 12), while that for ADHD was from 14 to 54% (mean = 1.6 (range 0-6) out of 6). Only 50% of the sites advised readers to clarify information with an appropriate health professional. Interrater reliability in pro forma scores for schizophrenia and ADHD was high (r = 0.96 and 0.95, respectively, p < 0.0001). Sites in the top 10% of scores were significantly more likely to be owned by an organization or have an editorial board than those in the bottom 10%. CONCLUSIONS: The Internet contains misleading information on both schizophrenia and ADHD. The methodology used in this paper could be adapted for other psychiatric conditions.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".