The co-design and co-creation of an integrated geriatric care system: A case study from Ontario, Canada, in partnership with older adult patients and providers
Bibliographic record
Abstract
Background: Integrated care has been heavily researched, but additional efforts are required to move knowledge of integrated care into action, ideally at a larger scale and in a more sustainable fashion. We need a stronger grasp of how integrated care models are implemented, and how they might be tailored to unique contexts. Within the context of complex health care interventions, it is relatively rare that researchers engage in evaluations of the process (Lewin et al., 2009); the focus tends to be the outcomes not the process (Moore et al., 2015). To support the implementation of sustained and tailored models of integrated care, we must endeavor to better capture the process in which change is enacted. This is particularly prudent in countries with aging societies, as health care integration is especially important for older adults, whose care needs cut across a range of systems, providers, and settings. Purpose: This work seeks to understand the knowledge to action (KTA) process in which knowledge is co-created within a co-design setting, and then implemented within a health region to improve integrated care for older adults. Here, we focus on the processes and results of the co-design approach with care providers and older adults. Older adult patient partners have been integrated in each step of this process. Methods: In this study, we have partnered with health care providers and older adult patients and caregivers in Southwestern Ontario, Canada, to document and evaluate efforts to co-design and implement an integrated model of care for frail older adults in two regions. We have worked with each region to understand and document their co-design process from late 2020 through to the present. Employing a qualitative multiple case study design, we have observed and documented virtual co-design sessions which occurred monthly, in two regions, and conducted individual interviews regarding the co-design process with older adult (n=4) and health care provider (n=9) working group members. Within these co-design sessions, we also facilitated the development and identification of goals for each region’s approach to integrating geriatric care. Qualitative data were analyzed using appropriate coding techniques. Results: Through observations and interviews with older adult and health care provider co-design working group members, a number of themes emerged. First, working group (WG) members identified that having an external facilitator leading the co-design sessions was a benefit. Co-design sessions needed to be flexible, informative, and productive to keep members engaged in the co-design work. WG members also identified a number of goals that needed to be achieved to indicate that co-designed improvements were made to the geriatric health system. Goals included, decrease referral duplication, improve patient and caregiver experience, improve provider collaboration, and decrease service wait time. Conclusion: This work has demonstrated practices and techniques for both creating and then tailoring an integrated care model for unique regions, including a novel application of the GAS guide as a tool to support co-design. These learnings may facilitate the future participation of older adults and their caregivers in improving health care, in Canada and elsewhere.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.039 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".