Collaborative Mental Health Care in Canada: Challenges, Opportunities and New Directions
Bibliographic record
Abstract
BackgroundIn 1997, the Canadian Psychiatric Association (CPA) and the College of Family Physicians of Canada (CFPC) published a position paper 1 highlighting the importance of improving collaboration between family physicians and psychiatrists and proposing ways in which this could be advanced.In 2011, an updated position paper reviewed the growing evidence, defined principles to guide collaboration and the external changes required to support it, broadened the scope of collaboration to include all mental health and primary care providers and services, and made recommendations for future priorities. 2 Since that time, collaborative mental health care (CMHC) has played a greater role in the planning and organization of Canadian health-care systems.3 There is a growing recognition of its potential to improve access to care (especially for marginalized and underserved populations), to integrate physical and mental health care, and to facilitate transitions in care.2,[4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] Evolving models of care are increasingly informed by evaluation data and the experiences of individuals with lived experience and
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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 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".