Building a Better System to Support Family Caregivers: Engaging Multi-level Interdisciplinary Stakeholders in Micro, Meso, and Macro Level Learning Health System Design.  
Bibliographic record
Abstract
The current Canadian healthcare workforce shortage and care crisis demands innovative solutions, yet it takes 17 years for healthcare innovations to reach 50% adoption in clinical practice. Healthcare workforce shortage solutions include expanding team-based care, moving integrated services closer to home[, and including Family Caregivers [FCGs] as partners on the care team, rather than treating them as visitors or patient accompaniments. Integrating FCGs as partners on the healthcare team improves patient care, health, and well-being, reduces the length of hospital stays, supports aging in the right place, promotes effective communication, and reduces healthcare providers’ workload. While FCGs and the people they care for require integrated care and services coordinated around their comprehensive needs, research confirms that FCGs remain invisible, and often marginalized by healthcare systems. Aim: Detail the micro-meso-macro-level elements and roles needed for building integrated FCG support systems. Results: As FCGs, the people they care for, and their care trajectories are diverse, an integrated system to support FCGs includes: 1) macro-level policy leadership 2) meso-level FCG-friendly communities, community organizations, health and social care systems, and providers trained to support FCGs; 3) micro-level assessment of FCGs needs and supports tailored to needs/goals, and 4) evaluation and practice improvement research. Three critical elements to Health Learning Approaches are: 1) Prioritize service needs and a design improvement approach; 2) Strengthen and link the assets required to operationalize rapid learning and a quality improvement approach, and 3) Establish a network of rapid-learning champions to drive and sustain action. Our Alberta-based Caregiver-Centered Care Research Program works at collaborating with stakeholders interested in enacting a collective impact approach to build a better system of all the essential elements of integrated health and social care programs and services needed to support FCG's well-being and sustain their caregiving. First, we convened a series of consultations to prioritize approaches to support FCGs. Stakeholders prioritized competency-based education for healthcare providers about person-centered care for FCGs. Accordingly, we engaged FCGs, researchers, health and social care providers/leaders, and policymakers to co-design and deliver free, online Foundational and Advanced education about providing person-centered care for FCGs to health and social care providers. Now, we need to strengthen our network and link assets. We convened a series of six webinars designed to raise awareness of FCGs' work, the need for systemic FCGs support, and to consult stakeholders about the next steps to integrate health and social care systems to support FCGs (October 20 – November 29, 2022). The webinars have also built our network of Canadian collaborators. Conclusions: The collaboration required to build a better system to support Canadian FCGs takes facilitation and time. The learning health system framework seems appropriate to approach the complex system and culture changes required more efficiently and effectively. Implications: Learning Health Systems and co-design by communities of patients, FCGs, health and social care providers/leaders, and researchers are currently regarded as a superior approach to improving healthcare and health outcomes. A strength of our team is collaborative, trusting relationships.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".