Person-Centered Care for Family Caregivers: Co-Designing an Education Program for the Healthcare Workforce
Bibliographic record
Abstract
Abstract Background: Research recommends the healthcare workforce receive competency-based education to support family-caregivers [FCGs}. typically, education has been directed at FCG’s to increase their care skills rather that at healthcare providers to provide person-centered care to FCGs. Objectives: We 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. Approach: Co-design is the act of creating with stakeholders to ensure useable results that meet stakeholder’s needs. We began by coining the concept “caregiver-centered care,” defined as a collaborative working relationship between families and healthcare providers aimed at supporting FCGs in their caregiving role, decisions about care management, and advocacy. From this definition we co-designed, then validated the Caregiver-Centered Care Competency Framework in a Delphi Process. Stakeholders (n= 101) including FCGs, providers, policy makers, community organizations, researchers, and educational designers then used effective practices for health workforce education to co-design the ‘foundational’ level of a Caregiver Centered Care education. 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 four months online, 815healthcare providers completed the education. 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 healthcare settings.
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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 | 0.045 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".