Co-Designing Caregiver-Centered Care: Training the Health Workforce to Support Family Caregivers
Bibliographic record
Abstract
Abstract 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’ wellbeing, is to educate the health workforce in a meaningful manner based on evidence. We validated Caregiver-Centered Care Core Competencies required to address the gap between what FCGs report they need and preparation of healthcare providers to meet those needs. This competency-based education will prepare healthcare providers to effectively identify, engage, assess, and support FCGs, and address the inconsistent system of supports for FCGs. We co-designed our Caregiver Centered Care Education using effective practices for dementia education for health workforce and co-design. We engaged over 60 multi-level, interdisciplinary stakeholders with expertise in health workforce education, frontline healthcare, dementia care, health policy, and family caregiving. We ensured that we included FCGs of people living with dementia. The teaching/learning resources include competency-aligned educational modules, multimedia resources, and facilitators guide. As the hallmark of effective education is content relevant to learners’ needs and contexts, our education is designed flexibly, to be tailored to settings and learners. We are pilot testing the Caregiver-Centered Care Education, for acceptability and effectiveness, in five contexts: primary care, acute care, homecare, supportive living, and long-term care. Our education will support Caregiver-Centered Care in all settings providing dementia-related care. Health workforce support will be essential to maintain FCG wellbeing and sustain family caregiving.
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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 | 0.014 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".