Child and youth mental health needs and service utilization during COVID-19.
Bibliographic record
Abstract
The COVID-19 pandemic has significantly impacted the lives of children, youth, and their families and their access to mental health services through new virus containment measures, such as social distancing guidelines, school and service closures, and the emergence of online mental health services. In this study, we utilized a total of 47,417 interRAI assessments to evaluate the impact of the current pandemic on the mental health service needs and referral trends of children and youth across 55 mental health service agencies in Ontario, Canada. Child and youth assessments that were conducted in the period of March 1 to October 31 in 2018 and 2019 were compared with the same period in 2020 to assess pandemic-related differences. Findings demonstrated that mental health assessments among children and youth decreased significantly during the pandemic period, with the greatest decline observed among children from low-income neighborhoods. Results also demonstrated a change in the proportion of mental health concerns previously seen in children and youth. Specifically, referrals for anxiety, depression, self-harm, problem videogaming, and Internet use increased, whereas referrals related hyperactivity/distractibility, risk of harm to others, aggressive, and disruptive behavior decreased. A decrease in bullying and increase in domestic violence exposure was also noted during the pandemic, with no reported differences in children and youth's experiences of physical abuse, sexual abuse, emotional abuse, and neglect. The clinical implications for children, youth, and their families, especially those within more vulnerable communities, are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".