Evaluating the psychosocial status of BC children and youth during the COVID-19 pandemic: A MyHEARTSMAP cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Understanding the psychosocial status of children and adolescents during the COVID-19 pandemic is vital to the appropriate and adequate allocation of social supports and mental health resources. This study evaluates the burden of mental health concerns and the impact of demographic factors while tracking mental health service recommendations to inform community service needs. METHODS: MyHEARTSMAP is a digital self-assessment mental health evaluation completed by children and their guardian throughout British Columbia between August 2020 to July 2021. Severity of mental health concerns was evaluated across psychiatric, social, functioning, and youth health domains. Proportional odds modelling evaluated the impact of demographic factors on severity. Recommendations for support services were provided based on the evaluation. RESULTS: We recruited 541 families who completed 424 psychosocial assessments on individual children. Some degree of difficulty across the psychiatric, social, or functional domains was reported for more than half of children and adolescents. Older youth and those not attending any formal school or education program were more likely to report greater psychiatric difficulty. Girls experienced greater social concerns, and children attending full-time school at-home were more likely to identify difficulty within the youth health domain but were not more likely to have psychiatric difficulties. Considerations to access community mental health service were triggered in the majority (74%) of cases. CONCLUSIONS: Psychosocial concerns are highly prevalent amongst children and adolescents during the COVID-19 pandemic. Based on identified needs of this cohort, additional community health supports are required, particularly for higher risk groups.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".