Community Volunteers and Primary Care Providers Supporting Older Adults in System Navigation: A Mixed Methods Study
Bibliographic record
Abstract
Introduction: Primary care providers and community volunteers have important roles in supporting patient system navigation and utilization of community-based health and social services (CBHSS). This study aimed to explore the experiences and impacts of system navigation in a complex intervention supporting older adults. Methods: We used a convergent mixed methods design. Participants included primary care team members (n = 67), community volunteers (n = 38), and programme clients (n = 128) across six communities in Ontario, Canada. Data sources included focus groups, interviews, system navigation function survey for volunteers, CBHSS use survey for clients, and implementation data on CBHSS recommended by providers and volunteers and used by clients. Results: Results showed the different patterns of how CBHSS categories were recommended and ultimately used. Exercise-related CBHSS were both recommended and used, independence-related CBHSS were mostly only recommended with less uptake, and chronic health condition and diet/nutrition CBHSS were most often used by clients. Discussion: Primary care teams' practice of system navigation was impacted by programme participation, including through learning about local CBHSS. However, volunteers felt more confident in tasks that did not include connecting to CBHSS. The programme did seem to result in many referrals, though the actual client uptake tended to be to more clinical rather than healthy lifestyle resources.
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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 | 0.016 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".