Implementing volunteer-navigation for older persons with advanced chronic illness (Nav-CARE): a knowledge to action study
Bibliographic record
Abstract
BACKGROUND: Nav-CARE is a volunteer-led intervention designed to build upon strategic directions in palliative care: a palliative approach to care, a public health/compassionate community approach to care, and enhancing the capacity of volunteerism. Nav-CARE uses specially trained volunteers to provide lay navigation for older persons and family living at home with advanced chronic illness. The goal of this study was to better understand the implementation factors that influenced the utilization of Nav-CARE in eight diverse Canadian contexts. METHODS: This was a Knowledge to Action study using the planned action cycle for Nav-CARE developed through previous studies. Participants were eight community-based hospice societies located in diverse geographic contexts and with diverse capacities. Implementation data was collected at baseline, midpoint, and endpoint using qualitative individual and group interviews. Field notes of all interactions with study sites were also used as part of the data set. Data was analyzed using qualitative descriptive techniques. The study received ethical approval from three university behavioural review boards. All participants provided written consent. RESULTS: At baseline, stakeholders perceived Nav-CARE to be a good fit with the strategic directions of their organization by providing early palliative support, by facilitating outreach into the community and by changing the public perception of palliative care. The contextual factors that determined the ease with which Nav-CARE was implemented included the volunteer coordinator champion, organizational capacity and connection, the ability to successfully recruit older persons, and the adequacy of volunteer preparation and mentorship. CONCLUSIONS: This study highlighted the importance of community-based champions for the success of volunteer-led initiatives and the critical need for support and mentorship for both volunteers and those who lead them. Further, although the underutilization of hospice has been widely recognized, it is vital to recognize the limitations of their capacity. New initiatives such as Nav-CARE, which are designed to enhance their contributions to palliative care, need to be accompanied by adequate resources. Finally, this study illustrated the need to think carefully about the language and role of hospice societies as palliative care moves toward a public health approach to 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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".