Assessment of change and persistence of youth psychosocial status reported by youth and their guardians during the COVID-19 pandemic: A MyHEARTSMAP study
Bibliographic record
Abstract
BACKGROUND: The pediatric mental health crisis pre-dated the COVID 19 pandemic with rates of mental health visits to pediatric emergency departments steadily increasing for the last decade. The COVID-19 pandemic has profoundly impacted children and adolescents and understanding the trajectory of their psychosocial status is important for appropriate resource allocation and policy planning. METHODS: MyHEARTSMAP is a digital self-assessment mental health evaluation that examines four major psychosocial domains: psychiatry, social, function, and youth health. Children and adolescents throughout British Columbia, and their guardians, completed the baseline assessment between August 2020 and July 2021 (51.8% completed by guardian only, 40.2% youth and guardians, 7.9% youth only). Both children and their guardians repeated the MyHEARTSMAP evaluation three-months after their baseline. Patient demographics and psychosocial concerns were statistically described and compared between baseline and follow-up. A logistic regression model assessed the influence of baseline scores and demographic factors on follow-up severity. RESULTS: 241 of 424 participants (56.8%) completed both the baseline and three-month follow-up. The majority of participants reported no change overtime across the psychosocial domains. Both improvement and decline occurred in each domain, with a greater proportion of psychosocial states improving rather than worsening, for all domains. Higher severity of psychosocial concerns reported at baseline indicated a greater likelihood of psychosocial concerns at 3-month follow-up for psychiatric, social and function concerns. Demographic, pandemic, and support service variables were not associated with psychosocial trajectories. CONCLUSIONS: The severity of youth mental health concerns in British Columbia remained consistent through three-month follow up, despite the changing nature of the COVID-19 pandemic during this period. Greater persistence of psychosocial concerns with increased severity highlights the need for early intervention to prevent worsening mental health. Community support is needed for youth experiencing mental health concerns to address mild psychosocial concerns before presentation at the emergency department.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".