A Survey of the Quality of Web Based Information on the Treatment of Schizophrenia and Attention Deficit Hyperactivity Disorder
Why this work is in the frame
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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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it