Person-Centered Care for Family Caregivers: Evaluating an Education Program for the Healthcare Workforce
Bibliographic record
Abstract
Abstract Background: While family caregivers [FCGs] provide 75-90% of care for people living in the community, most healthcare providers are not trained to provide person-centered care to FCGs. We followed research recommendations that the healthcare workforce receive competency-based education to identify, assess, support and partner with FCGs. Objective: Mixed methods evaluation healthcare workforce education program. Approach: We began by coining the concept “caregiver-centered care,” defining it as a collaborative working relationship between families and healthcare providers aimed at person-centered support for FCGs. From this definition, interdisciplinary stakeholders including FCGs (n=101) co-designed the Foundational Caregiver-Centered Care education. Learning resources included six competency-aligned educational modules with videos and interactive exercises that encourage reflection. Kirkpatrick Barr’s healthcare training evaluation framework underpinned our mixed methods evaluation. We measured participant’s reaction to the education (Level 1) and changes in learner’s knowledge and confidence to work with FCGs (Level 2). Results: 352 healthcare providers completed the education online (caregivercare.ca). Learners were satisfied with quality of education (Mean 4.75/5; SD=.5) and the education increased their motivation to learn more about caregiver-centered care (Mean 4.75/5; SD .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. Conclusion: The Caregiver-Centered Care education provides a foundation for educating healthcare providers working with FCGs to provide care to FCGs to maintain their wellbeing and sustain 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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".