Coping, fostering resilience, and driving care innovation for autistic people and their families during the COVID-19 pandemic and beyond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The new coronavirus disease (COVID-19) pandemic is changing how society operates. Environmental changes, disrupted routines, and reduced access to services and social networks will have a unique impact on autistic individuals and their families and will contribute to significant deterioration in some. Access to support is crucial to address vulnerability factors, guide adjustments in home environments, and apply mitigation strategies to improve coping. The current crisis highlights that our regular care systems are not sufficient to meet the needs of the autism communities. In many parts of the world, people have shifted to online school and increased use of remote delivery of healthcare and autism supports. Access to these services needs to be increased to mitigate the negative impact of COVID-19 and future epidemics/pandemics. The rapid expansion in the use of telehealth platforms can have a positive impact on both care and research. It can help to address key priorities for the autism communities including long waitlists for assessment and care, access to services in remote locations, and restricted hours of service. However, system-level changes are urgently needed to ensure equitable access and flexible care models, especially for families and individuals who are socioeconomically disadvantaged. COVID-19 mandates the use of technology to support a broader range of care options and better meet the diverse needs of autistic people and their families. It behooves us to use this crisis as an opportunity to foster resilience not only for a given individual or their family, but also the system: to drive enduring and autism-friendly changes in healthcare, social systems, and the broader socio-ecological contexts.
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.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it