Optimizing the integration of family caregivers in the delivery of person-centered care: evaluation of an educational program for the healthcare workforce
Bibliographic record
Abstract
BACKGROUND: While family caregivers provide 70-90% of care for people living in the community and assist with 10-30% of the care in congregate living, most healthcare providers do not meaningfully involve family caregivers as partners in care. Recent research recommends that the healthcare workforce receive competency-based education to identify, assess, support, and partner with family caregivers across the care trajectory. OBJECTIVE: This paper reports a mixed-methods evaluation of a person-centered competency-based education program on Caregiver-Centered Care for the healthcare workforce. METHODS: This foundational education was designed for all healthcare providers and trainees who work with family caregivers and is offered free online (caregivercare.ca). Healthcare providers from five healthcare settings (primary, acute, home, supportive living, long-term care) and trainees in medicine, nursing, and allied health were recruited via email and social media. We used the Kirkpatrick-Barr health workforce training evaluation framework to evaluate the education program, measuring various healthcare providers' learner satisfaction with the content (Level 1), pre-post changes in knowledge and confidence when working with family caregivers (Level 2), and changes in behaviors in practice (Level 3). RESULTS: Participants were primarily healthcare employees (68.9%) and trainees (21.7%) and represented 5 healthcare settings. Evaluation of the first 161 learners completing the program indicated that on a 5-point Likert scale, the majority were satisfied with the overall quality of the education (Mean(M) = 4.69; SD = .60). Paired T-tests indicated that out of a score of 50, post-education changes in knowledge and confidence to work with family caregivers was significantly higher than pre-education scores (pre M = 38.90, SD = 6.90; post M = 46.60, SD = 4.10; t(150) = - 16.75, p < .0001). Qualitative results derived from open responses echoed the quantitative findings in satisfaction with the education delivery as well as improvements in learners' knowledge and confidence. CONCLUSION: Health workforce education to provide person-centered care to all family caregivers is an innovative approach to addressing the current inconsistent system of supports for family caregivers. The education program evaluated here was effective at increasing self-reported knowledge and confidence to work with family caregivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".