Bibliographic record
Abstract
With 5 billion dollars in new federal funding to improve access to mental health services set to roll out over the next 10 years, a window of opportunity has opened to begin to close the long-standing gap in mental health funding in Canada. Public spending on mental health in Canada is only 7% of public spending on health overall (Jacobs et al., 2010), well short of the 9% called for in the Changing Directions, Changing Lives: The Mental Health Strategy for Canada (MHCC, 2012). This percentage is also well short of the disease burden comprised by mental illnesses, which ranges from 13% globally (WHO, 2011) to 23% in the UK (OECD, 2014). By comparison, recent figures from the Organisation for Economic Cooperation and Development (OECD, 2014) indicate that some countries devote as much as 18% of their health spending to mental health, with the UK sitting at 13%. Even with new targeted federal funding, closing, or at least narrowing, this gap will require careful attention to lessons learned in the past. This article explores how the gap in mental health funding came about in Canada and provides a more detailed analysis of the size of the gap itself. While it is now clear that the federal government will introduce a transfer that is directly targeted to mental health, there are still many policy options to consider for moving forward with next steps, including provincial/territorial contributions, accountability mechanisms, outcome measures, the insurance/financing model, and how tightly eligible expenses are tied to specific initiatives, population groups, or levels of evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".