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Record W4291149082 · doi:10.1177/13623613221114280

Mobile and online consumer tools to screen for autism do not promote equity

2022· article· en· W4291149082 on OpenAlexaboutno aff
Benjamin Sanders, Steven Bedrick, Sarabeth Broder‐Fingert, Shannon A Brown, Jill K. Dolata, Éric Fombonne, Julie A. Reeder, Luis Andres Rivas Vazquez, Plyce Fuchu, Yesenia Morales, Katharine E. Zuckerman

Bibliographic record

VenueAutism · 2022
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
FundersU.S. National Library of MedicineNational Institute of General Medical SciencesNational Institute of Mental Health
KeywordsAutismAutism spectrum disorderPsychologyThe InternetToddlerLiteracyReading (process)Equity (law)Developmental psychologyWorld Wide WebComputer sciencePolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.088
GPT teacher head0.373
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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