Autistic characteristics and mental health symptoms in autistic youth during the first <scp>COVID</scp>‐19 wave in Canada
Bibliographic record
Abstract
Autistic youth are at heightened risk for mental health issues, and pandemic-related stressors may exacerbate this risk. This study (1) described caregiver-reported youth mental health prior to and during the pandemic; and (2) explored individual, caregiver, and environmental factors associated with changes in autistic characteristics, social-emotional symptoms, and overall mental health. 582 caregivers of autistic children (2-18 years old) completed an online survey between June and July 2020 in which they provided demographic information, their child's pre-COVID and current mental health, autistic characteristics, and social-emotional symptoms. Caregivers also rated their own perceived stress, and COVID-related household and service disruption. According to caregivers, youth experienced more autistic characteristics and social-emotional concerns during the pandemic. Autistic youth were also reported to experience poorer overall mental health during the pandemic than before the pandemic. Older youth whose caregiver's indicated higher perceived stress and greater household disruption were reported to experience more autistic traits during pandemic. Caregiver-reported increases in youth social-emotional symptoms (i.e., behavior problems, anxiety, and low mood) was associated with being older, the presence of a pre-existing mental health condition, higher caregiver stress, and greater household and service disruption. Finally, experiencing less household financial hardship prior to COVID-19, absence of a pre-existing psychiatric condition, less caregiver stress, and less service disruption were associated with better youth pandemic mental health. Strategies to support the autistic community during and following the pandemic need to be developed. The developmental-ecological factors identified in this study could help target support strategies to those autistic youth who are most vulnerable to mental health problems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".