Mental health profiles of autistic children and youth during the COVID-19 pandemic
Bibliographic record
Abstract
Objectives: Canadian province-wide lockdowns have challenged children's mental health (MH) during the COVID-19 pandemic, with autistic children being at particular risk. The purpose of our study was to identify sub-groups of autistic children with distinct mental health change profiles, to understand the child-, parent-, and system-specific factors associated with such profiles in order to ultimately inform future interventions. Methods: Data were drawn from a large Canadian cohort (N=1,570) across Ontario, resulting in 265 autistic children (mean age=10.9 years, 76% male). K-means clustering analyses were employed to partition distinct MH profiles in six MH measures (mood, anxiety, OCD symptoms, irritability, inattention, hyperactivity) and group differences were examined with reference to the above factors. Additionally, we investigated the characteristics of children who accessed acute MH services. Results: The optimal number of clusters was two; one included those experiencing MH deterioration across all six MH measures (61.3%, 95% confidence interval [CI]=54.9 to 67.4), and a second included youth that did not experience MH changes (38.7%, 95%CI=32.6 to 45.1). Child-specific factors associated with MH deterioration included higher pre-existing internalizing symptoms, high levels of COVID stress. Parental MH challenges and system-specific factors, such as the loss of learning supports, access to physicians and material deprivation, were also associated with MH deterioration. Access to acute MH services were primarily associated with financial insecurity and loss of services. Conclusions: More than half of autistic children experienced MH deterioration, and person-specific (pre-existing MH, COVID related stress), parent-specific (Parent MH) and system-level (loss of services and material deprivation) characteristics were associated with such decline, providing clinical and policy opportunities for intervention at multiple levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".