Expenditures on Mental Health and Addictions for Canadian Provinces in 2003/04
Bibliographic record
Abstract
OBJECTIVE: To measure total public and private expenditures on mental health in each province. METHOD: Data for expenditures on mental health services were collected in the following categories: physician expenditures (general and psychiatrist fees for service and alternative funding), inpatient hospital (psychiatric and general), outpatient hospital, community mental health, pharmaceuticals, and substance abuse. Data for 2 years, 2003 and 2004, were collected from the Canadian Institute for Health Information (hospital inpatient and fees for service physicians), the individual provinces (pharmaceuticals, alternative physician payments, hospital outpatient, and community), and the Canadian Centre on Substance Abuse. Totals were expressed in terms of per capita and as a percentage of total provincial health spending. RESULTS: Total spending on mental health was $6.6 billion, of which $5.5 billion was from public sources. Nationally, the largest portion of expenditures was for hospitals, followed by community mental health expenses and pharmaceuticals. This varied by province. Public mental health spending was 6% of total public spending on health, while total mental health spending was 5% of total health spending. CONCLUSIONS: Canadian public mental health spending is lower than most developed countries, and a little below the minimum acceptable amount (5%) stated by the European Mental Health Economics Network.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".