A Living Laboratory Supporting Individuals with Complex Care Needs: The Centre for Research in Integrated Care 
Bibliographic record
Abstract
Individuals with complex care needs (CCNs) require more health and social care services than the average population. These individuals have better outcomes if they have access to integrated services across settings and sectors. Although promising research on integrated care is underway, efforts are typically not coordinated and occur without patient partner involvement. The critical need for collaboration among stakeholders in this field prompted the establishment of the Centre for Research in Integrated Care (CRIC) in 2018. CRIC is a living laboratory that develops and evaluates integrated care models that are patient-centred and based on the needs of individuals with CCNs. We engage with various stakeholder groups across Canada, including clinicians, decision makers, researchers, trainees, and patient partners in a setting where innovative ideas are explored and modified as needed throughout the research process. We use a patient-oriented research approach, which engages patient partners throughout the entire research process. Together we identify research priorities; design, implement, and evaluate programs; and share our findings. Our team conducts needs assessments on gaps and barriers to care and then develops, implements, and evaluates integrated care models to address these gaps and barriers. One example is a navigation centre we created for children with CCNs and their families. This was implemented in partnership with a patient and family advisory council and based on a needs assessment that involved over 120 interviews with children, their families, and care providers. In this model, patient navigators worked with clients to proactively guide and support them, matching unmet needs to appropriate resources. We have since spread this innovation to support people with dementia and their care partners. Our research demonstrates that navigation programs are effective in decreasing fragmentation, improving access, and promoting the integration of care. Our team also conducts research in close collaboration with the PriCARE Team led by Dr. Catherine Hudon on case management, one of the most studied and recommended models of integrated care. In this model, a case manager is assigned to patients, and they work collaboratively with the care team and patient to assess, plan, facilitate, and coordinate care. Patient partners have been actively engaged in all aspects of this research for 6+ years. We have found this intervention to be successful in improving patient outcomes, such as improved self-management, and also health system outcomes, such as decreased emergency room visits. In this poster presentation, we will share how CRIC creates an ecosystem where diverse stakeholders can co-create knowledge and solutions to contemporary health problems through the implementation of innovative and integrated health services across settings and sectors. We will also share how we apply our patient-oriented research approach to ensure that our studies are focused on meeting the needs of patients and improving their outcomes. Ultimately, our goal is to broaden our national scope and create new international partnerships so that we can expand the reach and impact of our research. The sustainability of healthcare systems worldwide is dependent on finding new ways to meet the needs of individuals who experience CCNs.
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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 | 0.047 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.008 | 0.039 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".