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Record W4229000742 · doi:10.1093/pch/pxab111

Mental health profiles of autistic children and youth during the COVID-19 pandemic

2022· article· en· W4229000742 on OpenAlexafffundabout
Μarina Charalampopoulou, Eun Jung Choi, Daphne J. Korczak, Katherine Tombeau Cost, Jennifer Crosbie, Catherine S. Birken, Alice Charach, Suneeta Monga, Elizabeth Kelley, Rob Nicolson, Stelios Georgiades, Muhammad Ayub, Russell Schachar, Alana Iaboni, Evdokia Anagnostou

Bibliographic record

VenuePaediatrics & Child Health · 2022
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsWestern UniversityMcMaster UniversityQueen's UniversityHospital for Sick ChildrenSickKids FoundationUniversity of TorontoInstitute for Clinical Evaluative SciencesHolland Bloorview Kids Rehabilitation Hospital
FundersCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Mental healthMedicineAutismPsychologyPsychiatryVirologyOutbreakInfectious disease (medical specialty)DiseasePathology

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.322
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2022
Admission routes3
Has abstractyes

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