Pinning Down the Accuracy Gap: Assessing Intervention Information About Dyslexia on Pinterest-Linked Web Pages
Bibliographic record
Abstract
Social media platforms such as Pinterest are a popular medium for locating and consuming health and mental health information, as well as educational resources to assist struggling learners. Despite parents and educators being frequent consumers of education-related information on Pinterest, no studies to date have explored the accuracy of intervention information for dyslexia on Pinterest-linked web pages, meaning that the extent to which it aligns with evidence-based practice and the science of reading is unclear. This study reviewed online information about interventions for dyslexia from 41 Pinterest-linked web pages to evaluate accountability, presentation, alignment with evidence-based practice, and readability using a set of standardized criteria. The quality of intervention information was generally poor, with websites meeting less than 10% of the standardized criteria. Further, most information was published by unspecified authors or authors without formal experience providing evidence-based interventions for dyslexia. Most sites also neglected to reference their sources or recommend follow-up with a professional. These findings suggest that psychologists should be steering educators away from Pinterest as a resource and towards more reliable websites. Possibilities for future research, and practical implications for school psychologists are discussed.
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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 | 0.021 | 0.179 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".