A Feasibility Study of a Volunteer Navigation Program in the Palliative Context
Bibliographic record
Abstract
Older persons with serious illnesses living in rural communities are an underserved population. They often live with heavy symptom burden and poor quality of life with limited access to resources. Nav-CARE (Navigation: Connecting Accessing Resourcing and Engaging) was developed to specially train and mentor volunteer navigators who help connect older persons with serious illness to the resources and information they need. Aims: This mixed methods longitudinal study evaluated Nav-CARE for feasibility, acceptability, ease of use, and satisfaction by older persons and volunteers. Methods: Nine volunteer navigators visited 23 older persons with serious illness every 3 to 4 weeks for 1 year. Data were collected from volunteer navigators, and older person participants at baseline, during the year- long implementation and post implementation. Results: Volunteer navigators and older persons reported Nav-CARE was easy to use, feasible and acceptable. The majority of older persons agreed or strongly agreed that they were satisfied with the navigation services (100%; 8/8), that navigation services were important to them (87%; 7/8), that they would recommend the program to someone else (87%; 7/8), and would participate in the program again (75%; 6/8). Similarly, volunteer navigators reported 100% (9/9) satisfaction with the program, 100% (9/9) would recommend it to others, and 67% (6/8) would participate again. Conclusions: Nav-CARE appears to be a feasible, acceptable, and satisfactory program for older persons with serious illness and volunteer navigators.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".