Advancing Collaborative Mental Health Care in Canada’s Primary Care Settings: A National Quality Framework with Recommended Measures
Bibliographic record
Abstract
Collaborative mental health care involves primary care providers and mental health specialists working together in structured ways to improve access to care, quality of care and outcomes. Collaborative mental health care is a proven approach to improve timely access to evidence-informed care, individual health outcomes, patient experience of care, equitable health care delivery and outcomes across populations, and cost-effectiveness of care. However, its implementation in Canadian primary care settings is variable and do not necessarily align with research evidence. This has led to a knowledge-to-practice gap regarding the comparative effectiveness of the different collaborative mental health care practices that teams and organizations may use to achieve desired outcomes. The Mental Health Commission of Canada (MHCC) and its partners are pleased to share a pan-Canadian evidence-based framework for planning and measuring quality collaborative care. The report, “Advancing Collaborative Mental Health Care in Canada’s Primary Care Settings: A National Quality Framework with Recommended Measures” is the result of a two-year project led by the Quality Improvement for Collaborative Care (QI4CC) research team and is based on extensive consultations with providers, planners, and people with lived experience. A qualitative study was used to explore the current state of collaborative care across Canada and determine how to define and measure its quality and the project team engaged a multi-stakeholder group throughout the entire project to get feedback on the research process and findings and to refine and finalize the framework. The resulting revised Quality Improvement for Collaborative Care (QI4CC) framework was developed to assist stakeholders in organizing and delivering primary care mental health and addictions services. The framework is intended to help leaders in practice and policy put in place needed supports and structures to achieve successful implementation of good practices in collaborative care as well as recommending quality measures to prioritize when evaluating collaborative care initiatives in primary care practices. Learning Objectives: 1. Have a better understanding of collaborative mental health care in primary care settings 2. Provide an overview of the Quality Improvement for Collaborative Care framework and the key components in delivery of effective collaborative mental health care 3. Identify recommended measures to evaluate and improve the quality of collaborative care in primary health settings 4. Explore opportunities to apply the QI4CC framework for an international audience Target Audience: health-care providers – especially those involved in delivering mental health care in primary care settings, administrative leaders of health services, policymakers, and champions of collaborative care in mental health and addictions services (includes people with lived and living experience)
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".