Person‐centered care for family caregivers of people living with dementia: Evaluating an education program for the healthcare workforce
Bibliographic record
Abstract
Abstract Background While family caregivers[FCGs] provide over 90% of care for people with dementia[1 2] most members of the healthcare workforce do not meaningfully involve FCGs as partners in care[3 4] or support FCGs in maintaining their own wellbeing. Recent research recommends the healthcare workforce receive competency‐based education to identify, assess, support and partner with FCGs across the care trajectory.[5 6] A multi‐level interdisciplinary team (n=101) co‐designed competency‐based person‐centered education for the health workforce. It is offered free online (caregivercare.ca) Objectives Report on a mixed methods evaluation of a person‐centered competency‐based education program for the healthcare workforce. Project description To create the program 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 [7 8]. From this definition, multi‐level, interdisciplinary stakeholders including FCGs (n=101) co‐designed a first or ‘foundational’ level of a Caregiver‐Centered Care education program. Teaching and learning resources include six competency‐aligned educational modules with videos and interactive exercises that encourage reflection. Methods The Kirkpatrick Barr[9 10] health workforce training evaluation framework underpinned our mixed methods evaluation design. We used the Student Satisfaction with Education Scale to measure participant’s reaction to the education (Level 1) and the Caregiver‐Centered Care Knowledge Assessment Test [CKAT] to assess changes in learner’s knowledge and confidence to work with FCGs (Level 2). Results In the first two months 352 healthcare providers completed the education through caregivercare.ca. Learners were satisfied with the overall quality of education (Mean 4.69 (SD=.6) Median 5). Student’s paired samples T‐test indicates pre‐post education changes in knowledge and confidence to work with FCGs were significant. Pre (M=37.8, Sd=7.6) to post (M=47.2, SD=3.5) t (125) = ‐14.39, p<.0005 (two‐tailed). Qualitative results derived from open responses mirrored the quantitative results. Discussion Educating the health workforce is a population health approach to address known gaps in supporting and working with FCGs across the care trajectory[2 3 11]. Conclusion The Care‐Giver Centered Care education provides a foundation for educating healthcare providers working with FCGs to provide person‐centered care to FCGs.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".