Abuse, Mental State, and Health Factors Pre and during the COVID-19 Pandemic: A Comparison among Clinically Referred Adolescents in Ontario, Canada
Bibliographic record
Abstract
Throughout the COVID-19 pandemic, population surveys revealed increased levels of anxiety and depression, while findings from large-scale population data analyses have revealed mixed findings with respect to the mental health consequences for children and youth. The purpose of this study was to examine the impact of the COVID-19 pandemic on the well-being and health-compromising behaviors of adolescents (12–18 years) previously referred for mental health services. Data were collected (pre-pandemic n = 3712; pandemic n = 3197) from mental health agencies across Ontario, Canada using the interRAI Child and Youth Mental Health assessment. Our findings revealed no increased incidence of witnessing domestic violence nor experiencing physical, sexual, or emotional abuse. Further, there were no increases in the risk of self-harm and suicide, anxiety, or depression among our sample of clinically referred youth. Finally, results demonstrated no increase in problematic videogaming/internet use, disordered eating, or alcohol intoxication, and a decrease in cannabis use. Our findings add to the growing body of knowledge as to the impact of the COVID-19 pandemic on children and youth. Further, findings underscore the importance of understanding the nuanced impact of the pandemic on various subgroups of children, youth, and families and highlight the need for continued monitoring of outcomes for these children and youth.
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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 | 0.001 |
| 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 | 0.001 | 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".