A mixed-method evaluation of a volunteer navigation intervention for older persons living with chronic illness (Nav-CARE): findings from a knowledge translation study
Bibliographic record
Abstract
Abstract Background Volunteer navigation is an innovative way to help older persons get connected to resources in their community that they may not know about or have difficulty accessing. Nav-CARE is an intervention in which volunteers, who are trained in navigation, provide services for older persons living at home with chronic illness to improve their quality of life. The goal of this study was to evaluate the impact of Nav-CARE on volunteers, older persons, and family participating across eight Canadian sites. Methods Nav-CARE was implemented using a knowledge translation approach in eight sites using a 12- or 18-month intervention period. A mixed method evaluation was used to understand the outcomes upon older person engagement; volunteer self-efficacy; and older person, family, and volunteer quality of life and satisfaction with the intervention. Results Older persons and family were highly satisfied with the intervention, citing benefits of social connection and support, help with negotiating the social aspects of healthcare, access to cost-effective resources, and family respite. They were less satisfied with the practical help available for transportation and errands. Older persons self-reported knowledge of the services available to them and confidence in making decisions about their healthcare showed statistically significant improvements (P
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".