How well do websites concerning children’s anxiety answer parents’ questions about treatment choices?
Bibliographic record
Abstract
The goals of this study were to evaluate the quality of information concerning anxiety disorders in children that is available on the Internet and to evaluate changes in the quality of website information over time. The authors identified websites addressing child anxiety disorders (N = 26) using a Google search and recommendations from an expert in child anxiety. Each website was evaluated on the extent to which it addressed questions that parents consider important, the quality of information, and the reading level. All websites provided adequate information describing treatment options; however, fewer websites had information addressing many questions that are important to parents, including the duration of treatment, what happens when treatment stops, and the benefits and risks of various treatments. Many websites provided inadequate information on pharmacological treatment. Most websites were of moderate quality and had more difficult reading levels than is recommended. Five years after the initial assessment, authors re-analyzed the websites in order to investigate changes in content over time. The content of only six websites had been updated since the original analysis, the majority of which improved on the three aforementioned areas of evaluation. Websites could be strengthened by providing important information that would support parent decision-making.
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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 | 0.013 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".