Co‐designing caregiver‐centered care health workforce competencies and training: Making the leap to support family caregivers of people living with dementia
Bibliographic record
Abstract
Abstract Background Family caregivers [FCGs] provide over 80% of the care for people with dementia, chronic illness and impairments. Despite evidence of their contributions and consequences of caregiving, support for FCGs has not been a health system priority. Our innovative solution to reduce caregiver distress and support caregivers to maintain their wellbeing is to educate the health workforce to identify, engage, and support family caregivers throughout the care trajectory. Education to prepare health providers to effectively identify, engage, assess, and support all FCGs is innovative approach to addressing inconsistent system of supports for FCGs. Objective: We will present the co‐design process and introduce our Health Workforce Caregiver‐Centered Care Education focused on dementia. To ensure a specific person‐centered focus on family caregivers, we created the term “caregiver‐centered care” defined as a collaborative working relationship between families and healthcare providers in supporting family caregivers in their caregiving role, decisions about services, care management, and advocacy. Project Description: We designed our Caregiver Centered Care Education using effective practices for dementia education for the health workforce. The Caregiver Centered Care Competency Framework validated in March, 2019 underpins the design and evaluation. Methods We engaged over 100 multi‐level, interdisciplinary stakeholders familiar with dementia care from diverse settings. We ensured that we included FCGs of people living with dementia, to co‐design Caregiver Centered Care Education for the Health Workforce. Results We co‐designed dementia focused competency‐based education modules aligned with the previously validated Caregiver‐Centered Competencies for the health workforce. The teaching and learning resources include competency‐aligned educational modules, multimedia resources, and facilitators guide that are designed flexibly, to be tailored to settings and learners. Discussion: The hallmark of effective education is content relevant to learners’ needs and contexts. We will pilot test the Caregiver‐Centered Care Education, for acceptability and effectiveness, in five contexts: primary care, acute care homecare, supportive living, and long‐term care. Conclusion Our education will support caregiver‐centered care in all settings providing dementia‐related care. Health workforce support will be essential to maintaining FCG wellbeing and sustaining the family caregiving that reduces health system costs.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".