Temporal Trends in the Distribution of Child and Youth Mental Ill-Health from 1983 to 2023: Evidence from 4 General Population Based Samples in Ontario
Bibliographic record
Abstract
Mental health problems can have a big impact on young people, with lifelong consequences. Our understanding of mental health has changed a lot over the last 40 years, with new ways to identify how many young people have mental health problems and better ways to measure how severe their mental health problems are. However, one of the challenges in understanding mental health problems in young people over the last 40 years is that different studies have used different ways to identify and measure mental health problems in young people. Some studies find that many more young people have mental health problems today than in the past. But other studies find that just the same amount of, or even less, young people have mental health problems today than in the past. Our study aims to provide some clear evidence on whether there is an increase, decrease, or no change in the level of mental health problems in young people today compared to 10 years ago, 25 years ago, and 40 years ago. We are using population-representative data that was collected to measure mental health problems in young people using the same items across Ontario. We will use this information to understand whether or how levels of mental health problems in young people have changed over 40 years. We also aim to understand if there are some groups of young people who are experiencing more mental health problems while there may be other groups of young people who are experiencing the same or less mental health problems. Finally, we aim to understand if the young people with the most mental health problems have become more unwell over time. Results from this study will help us know if there are more young people with mental health problems today compared to the past and will help policy-makers to support programs that will help young people who are experiencing mental health problems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".