Equipping Change Agents: Applying Mixed Methods to Learn About the Outcomes of the Co-Designed Caregiver-Centered Care Champions Education Program
Bibliographic record
Abstract
Family caregivers provide most daily care for people living with chronic illness or frailty, yet they remain under-recognized in health and social care systems. To address this gap, we co-designed the Caregiver-Centered Care Champions Education Program, which equips frontline providers with the competencies needed to lead caregiver-inclusive change. Guided by the Kirkpatrick-Barr Health Workforce Education Framework, we conducted a mixed methods interpretive description evaluation of learner satisfaction, knowledge and confidence gains, and self-reported behaviour change. Sixty-seven interdisciplinary participants completed three online modules. Quantitative results from pre/post surveys (Wilcoxon signed rank tests) showed significant improvements across all competencies (p < 0.001; large effect sizes) alongside high satisfaction (means 6.56–6.96/7). Qualitative findings revealed that 94% of participants applied program content within three months, and 61% implemented five or more distinct behaviour changes (e.g., collaborative care planning, system navigation support). The analysis illuminated how learners integrated caregiver-centred principles with change leadership strategies. Time constraints and staffing shortages emerged as key barriers. Our co-designed, theory-informed approach effectively bridged individual learning and system change, demonstrating the potential to transform caregiver inclusion practices when supported by organizational policies.
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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 | 0.108 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| 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".