Scaling out a palliative compassionate community innovation: Nav-CARE
Bibliographic record
Abstract
Background: There is an urgent need for community-based interventions that can be scaled up to meet the growing demand for palliative care. The purpose of this study was to scale out a volunteer navigation intervention called Nav-CARE by replicating the program in multiple contexts and evaluating feasibility, acceptability, sustainability, and impact. Methods: This was a scale-out implementation and mixed-method evaluation study. Nav-CARE was implemented in 12 hospice and 3 nonhospice community-based organizations spanning five provinces in Canada. Volunteers visited clients in the home approximately every 2 weeks for 1 year with some modifications required by the COVID-19 public health restrictions. Qualitative evaluation data were collected from key informants ( n = 26), clients/family caregivers ( n = 57), and volunteers ( n = 86) using semistructured interviews. Quantitative evaluation data included volunteer self-efficacy, satisfaction, and quality of life, and client engagement and quality of life. Findings: Successful implementation was influenced by organizational capacity, stable and engaged leadership, a targeted client population, and skillful messaging. Recruitment of clients was the most significant barrier to implementation. Clients reported statistically significant improvements in feeling they had someone to turn to, knowing the services available to help them in their community, being involved in things that were important to them, and having confidence in taking care of their illness. Improvements in clients’ quality of life were reported in the qualitative data, although no statistically significant gains were reported on the quality of life measure. Volunteers reported good self-efficacy and satisfaction in their role. Conclusion: The feasibility, acceptability, and sustainability of the program were largely dependent on strong intraorganizational leadership. Volunteers reported that their involvement in Nav-CARE enabled them to engage in ongoing learning and have a meaningful and relational role with clients. Clients and families described the positive impact of a volunteer on their engagement and quality of life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".