Mobile and online consumer tools to screen for autism do not promote equity
Bibliographic record
Abstract
Limited access to screening and evaluation for autism spectrum disorder in children is a major barrier to improving outcomes for marginalized families. To identify and evaluate available digital autism spectrum disorder screening resources, we simulated web and mobile app searches by a parent concerned about their child’s likelihood of autism spectrum disorder. Included digital autism spectrum disorder screening tools (a) were on Internet or mobile app; (b) were in English; (c) had a parent user inputting data; (d) assigned likelihood category to child <9 years; and (e) screened for autism spectrum disorder. Ten search terms, developed using Google Search and parent panel recommendations, were used to search web and app tools in the United States, the United Kingdom, India, Australia, and Canada using Virtual Private Networks. Results were examined for attributes likely to benefit parents in marginalized communities, such as ease of searching, language versions, and reading level. The four terms most likely to identify any tools were “autism quiz,” “autism screening tool,” “does my child have autism,” and “autism toddler.” Three out of five searches contained autism spectrum disorder screening tools, as did one of 10 links or apps. Searches identified a total of 1475 websites and 919 apps, which yielded 23 unique tools. Most tools required continuous Internet access or offered only English, and many had high reading levels. In conclusion, screening tools are available, but they are not easily found. Barriers include inaccessibility to parents with limited literacy or limited English proficiency, and frequent encounters with games, advertisements, and user fees. Lay Abstract Many parents wonder if their child might have autism. Many parents use their smartphones to answer health questions. We asked, “How easy or hard is it for parents to use their smartphones to find ‘tools’ to test their child for signs of autism?” After doing pretend parent searches, we found that only one in 10 search results were tools to test children for autism. These tools were not designed for parents who have low income or other challenges such as low literacy skills, low English proficiency, or not being tech-savvy.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".