Analysis of the quality of online resources for parents of children who are late to talk
Bibliographic record
Abstract
Background and aims: Internet usage worldwide has become a primary source of health-related information and an important resource for parents to find advice on how to promote their child's development and well-being. It is important that healthcare professionals understand what information is available to parents online to best support families and children. The current study evaluated the quality of online resources accessible for parents of children who are late to talk. Method: Fifty-four web pages were evaluated for their usability and reliability using the LIDA instrument and Health on the Net Foundation code of conduct certification, and readability using the Flesch Reading Ease Score and Flesch-Kincaid Grade Level. Origin, author(s), target audience, topics discussed, terminology used, and recommendations were also examined. Results: < 0.001) were found for sites with Health on the Net Foundation code of conduct certification. Readability fell within the standard range. The largest proportion of websites were American, written by speech-language pathologists, with the most common topics being milestones, tips and strategies, and red flags. Discrepancies were mostly seen in terminology and misinformation, and when present, usually related to risk factors and causes. Conclusion: Prior to recommending websites to parents, health professionals should consider readability of the content, check that information is up-to-date, and confirm website sources and reputable authorship. Health professionals should also be aware of the types of unclear or inaccurate information to which parents of children who are late to talk may be exposed online.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".