The Burden of Mental Illness and Addiction in Ontario
Bibliographic record
Abstract
OBJECTIVE: Public Health Ontario and the Institute for Clinical Evaluative Sciences have collaborated to estimate the burden of illness attributable to mental disorder and addictions in Ontario. METHODS: Health-adjusted life years were used to estimate burden. It is conceptually similar to disability-adjusted life years that were used in the global burden of disease studies. Data sources for the mental illnesses and addictions used in our study included health administrative data for the province of Ontario, survey data from Statistics Canada and the Centre for Addiction and Mental Health, vital statistics data from the Ontario Office of the Registrar General, and US epidemiologic survey data. RESULTS: The 5 conditions with the highest burden are: major depression, bipolar affective disorder, alcohol use disorders (AUDs), social phobia, and schizophrenia. The burden of depression is double the next highest mental health condition (that is, bipolar affective disorder) and is more than the combined burden of the 4 most common cancers in Ontario. AUDs were the only disease group that had a substantial proportion of burden attributable to early death. The burden estimates for the other conditions were primarily due to disability. CONCLUSIONS: The burden of these conditions in Ontario is as large or larger than other conditions, such as cancer and infectious diseases, owing in large part to the high prevalence, chronicity, and age of onset for most mental disorders and addiction problems. The findings serve as an important baseline for future evaluation of interventions intended to address the burden of mental health and addictions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".