How Collaborative Mentoring Networks Are Building Capacity in Primary Care
Bibliographic record
Abstract
The need for increased capacity in primary care to treat the growing numbers of patients with complex chronic health conditions is well established (Roberts et al. 2015). Meeting that need requires not only more family physicians but also more support and resources to handle challenging cases. The Collaborative Mentoring Networks (CMNs), created in 2001 by the Ontario College of Family Physicians and funded by the Ontario government, have provided that support and proven particularly successful in improving physicians' competence and confidence in caring for patients struggling with mental health, addictions and chronic pain. The networks give family physicians timely, ongoing access to mentors with greater clinical expertise. In 2017, the networks expanded from two to seven, spreading support to palliative and end-of-life care and medical assistance in dying and focusing on leadership in primary care, early years in practice and rural medicine. CMNs' early impact involved increased primary care capacity in family practice, better-supported family physicians treating more patients with complex conditions, fewer specialist referrals, less isolation and greater retention.
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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 | 0.036 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 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".