Family Involvement Training for Staff and Family Caregivers: Case Report on Program Design and Mixed Methods Evaluation
Bibliographic record
Abstract
The COVID-19 pandemic underscored the imperative for meaningful family involvement in long-term care, aligning with policy and safety standards while enhancing outcomes for caregivers, residents, and staff. The objectives of this article are as follows: (1) a case study report on implementing a family involvement intervention designed to facilitate the formal and safe engagement of family caregivers in resident care and (2) the pilot evaluation of the intervention. We used Knapp’s six-step implementation science model to guide and describe intervention development to provide insight for others planning family involvement projects. We employed sequential mixed methods, including surveys with quantitative and qualitative questions before and after program implementation for providers, and surveys and interviews with family caregivers a year after. We used the Mann–Whitney U test (p < 0.05) to assess differences in health providers’ perceptions pre- and post-education. Families and staff perceived that the Family Involvement Program was important for improving the quality of care, residents’ quality of life and family/staff relationships. Providers’ perceptions of the program’s positive impact on residents’ quality of life (p = 0.020) and quality of care (p = 0.010), along with their satisfaction with working relationships with families (p = 0.039), improved significantly after the program. Qualitative data confirmed improvements in family–staff relationships. In conclusion, we documented the design of this family involvement initiative to encourage family caregivers and staff to work together in residents’ care. Youville’s Family Involvement Program gives families and family caregivers an explicit role as partners in long-term care. The mixed methods pilot evaluation documented improvements in staff and family relationships.
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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 | 0.072 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".