Proposed Methods For Estimating Costs Of Mental Health In Canada (2007-2020)
Bibliographic record
Abstract
This report presents the results of an investigation by Greo Evidence Insights into how Canadian mental health (MH) costs could be estimated. It begins by conducting a review of studies estimating the costs of MH in Canada since 2010 and examines the various approaches employed. Based on this analysis the next section makes recommendations regarding cost types to include, the granularity of the estimates, and the approach to missing/ incomplete data. The report then recommends a phased approach to estimating the cost of mental health: Phase I describes in detail the data sources and methods to estimate public, direct health care costs associated with general and psychiatric MH-related hospitalizations and emergency room visits and non-hospital-based interventions (i.e., physician costs, pharmaceutical costs, community MH services). Phase II describes methods for estimating social and income support payments and indirect costs. Finally, Phase III describes data sources and methods for estimating private health and lost productivity costs.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".