9.3 Longitudinal Changes in Child and Youth Mental Health Symptoms During Distinct Phases of the COVID-19 Pandemic
Bibliographic record
Abstract
of this Symposium is to present new data from a number of epidemiological studies globally that use new methods, sampling frames, and analytic methods to build upon the work of the first generation of psychiatric epidemiological studies.Methods: Four presentations will focus on data from Ontario, Brazil, Greece, and the United States, and data from the Global Burden of Disease database.Each presentation will focus on innovations in sampling, measurement, data analysis, and data synthesis to go beyond simple estimates of prevalence and associations with risk and protective factors.Results: The results across the 4 presentations support the high prevalence of mental disorders among children and youth as well as highlighting important inequities across specific populations.Conclusions: A developmentally sensitive approach to estimating prevalence and morbidity associated with mental disorders among children and youth identifies important differences among age groups and certain social determinants of health.Given the implications of the early onset and lifetime burden of mental disorders, more resources need to be directed toward effective prevention and intervention initiatives for vulnerable children and youth in both high-income and low-and middle-income countries across the globe.
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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 | 0.004 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".