Person‐centered care for family caregivers of people living with dementia: Co‐designing an education program for the healthcare workforce
Bibliographic record
Abstract
Abstract Background Recent research recommends the healthcare workforce receive competency‐based education to identify, assess, support and partner with family‐caregivers [FCGs} across the care trajectory.[1 2] Although the risk of FCG anxiety, burden, and loneliness to FCG’s wellbeing is widely documented, typically education has been targeted towards FCG’s to increase their care skills rather to educate healthcare providers to support FCG’s caregiving and wellbeing.[3] Objectives We will present the co‐design process used to create a competency‐based education program for the healthcare workforce that ensures a person‐centered focus on FCGs and introduce our Health Workforce Caregiver‐Centered Care Education focused on dementia. Co‐design is the act of creating with stakeholders to ensure the results meet their needs and are usable. Project Description We began by coining the concept “caregiver‐centered care,” defining it as: a collaborative working relationship between families and healthcare providers aimed at supporting FCGs in their caregiving role, decisions about services, care management, and advocacy [4 5]. From this definition, and working with multi‐level interdisciplinary stakeholders we designed[6] and validated[7] a Caregiver‐Centered Care Competency Framework in a Modified Delphi Process. Stakeholders (n= 101) including FCGs, health providers, policy makers, community organizations, research team, script writer, and educational designers then used effective practices for dementia education for the health workforce [8‐11] to co‐design the first or ‘foundational’ level of a Caregiver Centered Care education program. Results Teaching and learning resources include six competency‐aligned educational modules with videos and interactive exercises that encourage reflection. With the COVID‐19 pandemic, we moved the education online (caregivercare.ca). In the first two months online, November 9, 2020‐January 9, 2021, 352 healthcare providers completed the education. To date, learners’ qualitative evaluations have been positive, “Very good information for professionals working with caregivers; especially relevant to homecare, geriatricians, allied health, and others working within the Seniors’ Health realm. Engaging format that really evokes empathy for caregivers.” Discussion We continue to use mixed methods to evaluate the Caregiver‐Centered Care Education, for acceptability and effectiveness, in five care contexts (primary, acute, home, supportive living, long‐term care). Conclusion We expect that our education will support caregiver‐centered care in all settings providing dementia‐related care.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".