Rethinking autism spectrum disorder assessment for children during <scp>COVID</scp>‐19 and beyond
Bibliographic record
Abstract
The COVID-19 pandemic has posed unique challenges for families and caregivers, as well as for autism-focused clinicians, who are faced with providing a thorough and accurate evaluation of children's specific needs and diagnoses in the absence of in-person assessment tools. The shift to telehealth assessments has challenged clinicians to reconsider approaches and assumptions that underlie the diagnostic assessment process, and to adopt new ways of individualizing standard assessments according to family and child needs. Mandates for physical distancing have uncovered deficiencies in diagnostic practices for suspected autism and have illuminated biases that have posed obstacles preventing children and families from receiving the services that they truly need. This Commentary outlines several considerations for improving diagnostic practices as we move forward from the current pandemic and continue to strive to build an adaptable, sustainable, equitable, and family-centered system of care. LAY SUMMARY: Physical distancing and the abrupt end to in-person services for many children on the autism spectrum has forced clinicians to examine the existing challenges with autism spectrum disorder (ASD) diagnostic assessment and consider things they want to keep and things that should be changed in the years ahead. New approaches such as telehealth both alleviated and exacerbated existing disparities, and brought into stark focus the importance of equitable and timely access to family-centered care. This commentary suggests ways of improving clinical practices related to ASD assessment to continue along this path.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".