Stepping towards integrated supports for family caregivers of people living with dementia: Engaging multi‐level interdisciplinary stakeholders in co‐design of competency‐based education
Bibliographic record
Abstract
Abstract Background Remaining home as long as possible or aging in place in their community home, particularly for those living with dementia, is highly dependent on family caregivers (FCGs) and health providers partnering with FCGs..[1] FCGs provide 90% of the care yet are marginalized within healthcare systems. Educating healthcare providers to support FCGs is a step towards addressing the inconsistent system of supports for diverse FCGs throughout variable trajectories.[2 3] Involving multilevel stakeholders in the educational co‐design process can help ensure the education is relevant for the healthcare providers who interact with FCGs.[4 5] Currently, moving best practices into healthcare is a time‐consuming process (10 to 17 years).[6 7] However, co‐design facilitates moving knowledge into practice more quickly. Objectives 1) Detail the co‐design processes and tools used to develop competency‐based Caregiver‐Centered Care Education for the health workforce. 2) Report on the key elements associated with successful co‐design. Method 106 multi‐level interdisciplinary stakeholders including FCGs, educators, researchers, healthcare providers and leaders, educational designers, not‐for‐profit leaders, policy influencers, and policymakers were involved in three co‐design phases: 1) Developing relationships and insights; 2) Translating insights into education design; and 3) Planning the implementation, spread, and scale‐up of the Modules. The research tools used in each of these phases included literature reviews, qualitative and survey research on specific topics, consultations (symposia, modified Delphi process, meetings), and mixed methods evaluation. Result Co‐design facilitated translation of best practices of Caregiver‐Centered Care into successful competency‐based training for the health workforce. Modules have been highly accessed by healthcare providers and trainees. Learners report very high satisfaction, relevance, usefulness, and significant knowledge gains upon completion. Four elements were critical to successful education co‐design: 1) an engaged co‐design team led by people knowledgeable about healthcare and FCGs; 2) co‐design team access to collaborators/staff with the appropriate theoretical, research, and facilitation skills; and 3) an expert educational design team to bring stakeholders’ ideas to life. Conclusion We leveraged stakeholders’ knowledge and insights to reduce the time to develop and scale an innovative population health approach in which healthcare providers are educated to support all FCGs throughout diverse care trajectories.
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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 | 0.029 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".