18 Building a better system to support family caregivers: co-designing health workforce education
Bibliographic record
Abstract
Background Innovative solutions are needed to address the healthcare workforce shortage and care crisis. This includes involving Family Caregivers (FCGs) as partners on the care team, rather than treating them as mere accompaniments. Integrating FCGs improves patient care, reduces hospitalizations, and eases healthcare providers’ workload. However, FCGs often remain invisible and marginalized by healthcare providers despite the need for integrated care that addresses their comprehensive needs. Objectives Report on our use of co-design and learning health systems approaches to building the essential elements of integrated supports for FCGs. Methods Our Alberta Caregiver-Centered Care Research Program collaborates with stakeholders to build integrated health and social care supports for FCGs. We use Learning Health System methods to improve FCGs’ population health: Micro level: Recognize and assess FCGs’ needs. Meso level: Foster health and social care partnerships and educate healthcare providers in person-centered care. Macro level: Implement coordinated policies to support FCGs. Results In a series of consultations, multi-level stakeholders prioritized person-centered care education for healthcare providers working with family caregivers. We co-designed Foundational and Advanced education, delivered free online. Using the Kirkpatrick-Barr framework, we evaluated the program’s impact on learner satisfaction (Level 1) and changes in knowledge, attitudes, and confidence (Level 2) (tables 1 and 2). Participants from all healthcare settings completed the education, showing high satisfaction (M=6.64; SD=.76) and significant improvements in post-education scores (pre M=60.45, SD=10.03; post M=67.30, SD=4.34; t(65)=-6.11, p<0.001) (figures 1 and 2). The learning health system approach helped us prioritize service needs and improvement design approach. Engaging multi-level interdisciplinary stakeholders in educational co-design developed champions to drive change and sustain action. Conclusions Co-design and health workforce education empowers providers to identify areas for improvement and implement changes that will enhance FCGs’ healthcare experience. The learning health system framework is a useful approach for addressing the complex system and culture changes required to support FCGs.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".