Improving Mental Healthcare by Primary Care Physicians in British Columbia
Bibliographic record
Abstract
This article describes a new and innovative training program to assist family physicians to better care for their patients with mental health conditions. Trained family physician leaders train other family physicians. The training package includes a wide range of tools that can be used by physicians in their own offices. Preliminary results indicate that physicians want to be trained, and data indicate a high degree of success for the training module. Some 91% of physicians who attended the training indicated that it had improved their practice, and 94% indicated that it had improved patient care. The training materials are online for those who wish to learn more. M ental health conditions are common in Canada, yet they remain difficult to diagnose and treat in the primary care setting. It is also difficult to obtain accurate and up-to-date data on the prevalence of mental illness. However, current estimates indicate that approximately 10% of Canadians experience a mental illness in a given year, and one in five may have a mental illness in their lifetime (Canadian Mental Health Association 2005; Mood Disorders Society of Canada 2009). In 2006, the Senate Committee on Social Affairs, Science and Technology released its report on mental health in Canada. This report resulted in the establishment of the Mental Health Commission of Canada in 2007 (Mental Health Commission 2010). In 2009, the commission released a framework for a Mental Health Strategy in Canada (Mental Health Commission of Canada 2009). This article presents information on a new and innovative learning module developed in British Columbia that trains family physicians to better care for their patients with mental health symptoms. The learning module is consistent with the seven goals enunciated in the Mental Health Strategy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".