An Expert Discussion on Autism in the COVID-19 Pandemic
Bibliographic record
Abstract
We are living in uncertain times. The COVID-19 pandemic, and the need to stay physically distant from each other, has required us to make very rapid changes to our everyday lives and wider society. The impact of the pandemic will likely be even more significant for autistic people—difficulties managing unexpected change and uncertainty, high risk of vulnerability, and health inequalities could all be magnified in the pandemic. However, with challenge and change can come opportunity. For years, disability advocates and their allies have campaigned for reasonable adjustments to enable autistic people to better access social spaces, health care, education, and employment. Adjustments we have identified and prioritized together with the autism community, such as making appointments and receiving therapy online, have not been implemented. However, in the current crisis, these adjustments have finally had to happen for everyone, and quickly. This could have the unintended but positive effect of finally addressing longstanding barriers for autistic people's inclusion in society that have been languishing for years. The current extent of the impact of the pandemic on autistic adults is unknown. An important first step is to identify and discuss the challenges and opportunities that the COVID-19 pandemic poses autistic adults, incorporating a variety of perspectives. This roundtable, therefore, aims to bring together autistic adults, their families, practitioners, and academics across the fields of disability rights, public health, medicine, psychology, and mental health across different countries and contexts. Our discussion focuses on what we need to be aware of to address the issues of interest to autistic adults in the pandemic now, how we can address these issues, and make tangible recommendations to be addressed in future research, policy, and practice.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".