Developing a System Navigator Role in Primary Care Using a Co-Design Approach
Bibliographic record
Abstract
Background: Older Canadians with chronic diseases are the biggest users of the health care system. Primary health care services are seen as having a role in coordinating health system access and care for older adults with chronic illness, but at present lacks specific strategies to fulfil this role. The Chronic Care Model (CCM) developed by Wagner and colleagues (1996) provides a framework for developing an effective health care system for chronic disease prevention and management (1). Fundamental to the CCM is the support of productive interactions of patients and families with health care providers, leading to improved outcomes. For older adults with chronic disease and their families, a system navigator role could support productive interactions and effective coordination and navigation through a complex and fragmented health care system. Aims: This project aims to understand how the role of a system navigator can be developed and implemented in primary care. Methods: This study used qualitative methods, within a developmental evaluation approach (2) to develop a system navigator role by working with multiple stakeholders in two communities (one urban, one rural) of Ontario, Canada. Focus group (n=4) and key informant (n=6) interviews with members of primary care teams, representatives of community support services, patients, and their families, were conducted to understand the context within which the primary care teams are operating, available community resources, and opportunities to support system navigation. Data were coded using a line by line, emergent approach (3). These results were reviewed in a workshop which brought primary care and community representatives together to refine the system navigation model and to identify strategies for its implementation. Results: The focus group and key informant interviews identified health care and community needs, resources and opportunities to support system navigation in each primary care centre. For example, community services such as meals on wheels, adult day programs, and exercise programs were identified and charted onto referral maps to be used by the system navigator to link older adults to appropriate resources. Primary care teams and community stakeholders worked in partnership to develop and implement a feasible model of system navigation, in which patients and families are engaged in decision-making around their care.
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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 | 0.071 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".