STEPS TOWARD RAISE: CO-DESIGN OF COMPETENCY-BASED EDUCATION TO ENGAGE FAMILY CAREGIVERS AS PARTNERS
Bibliographic record
Abstract
Abstract Meeting the needs of a growing population of older people living with complex conditions is highly dependent on healthcare providers partnering with family caregivers (FCGs). FCGs provide 90% of the care yet are marginalized within healthcare systems. Educating healthcare providers to support FCGs is a necessary step towards addressing the inconsistent system of supports for diverse FCGs throughout variable care trajectories. Current research suggests that co-design benefits stakeholders, produces superior outcomes, and facilitates moving knowledge efficiently into healthcare practices. Currently, moving best practices into healthcare is a time-consuming process (10-17 years). This presentation will discuss a feasible three-phase co-design process that included 120 multilevel interdisciplinary stakeholders including FCGs, educators, researchers, not-for-profit and healthcare providers/leaders, educational designers, and policy influencers/makers: 1) Developing relationships and insights; 2) Translating insights into education design; and 3) Planning the implementation, spread, and scale-up. The research tools used included literature reviews, qualitative and survey research on specific topics, consultations (symposia, modified Delphi process, co-design meetings), and mixed methods evaluation. Three modules, Foundational, COVID-19, and Advanced have been developed. Learners report high satisfaction, relevance, and significant knowledge gains upon completion. This successful education co-design required three critical elements: 1) an engaged co-design team led by people knowledgeable about healthcare and FCGs; 2) team access to collaborators/staff with the appropriate theoretical, research, and facilitation skills; and 3) an educational design team to bring stakeholders’ ideas to life. Leveraging stakeholders’ insights are a critical step towards the RAISE act goal of educating healthcare providers to include FCGs as partners-in-care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".