A new population-based measure of the economic burden of mental illness in Canada
Bibliographic record
Abstract
This paper presents a comprehensive measure of the incremental economic burden of mental illness in Canada which incorporates the use of medical resources and productivity losses due to long-term and short-term disability, as well as reductions in health-related quality of life (HRQOL), for the diagnosed and undiagnosed population with mental illness. The analysis was based on the population-based Canadian Community Health Survey Cycle 2.1 (2003). For all persons, we measured all health services utilization, longterm and short-term work loss, and health-related quality of life and their dollar valuations, with the economic burden being the difference in dollar measures between the populations with and without mental health problems. In total, the economic burden was $51 billion in 2003. Over one-half was due to reductions in HRQOL. The current accepted practice in economic assessments is to include changes in medical resource use, work loss, and reductions in HRQOL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".