Exploration of volunteers as health connectors within a multicomponent primary care‐based program supporting self‐management of diabetes and hypertension
Bibliographic record
Abstract
Volunteers support health and social care worldwide, yet there is little research on integrating these unpaid community members into primary care. 'Health Teams Advancing Patient Experience, Strengthening Quality through Health Connectors for Diabetes Management' (Health TAPESTRY-HC-DM) integrates volunteer 'health connectors' into a community- and primary care-based program supporting client self-management in Hamilton, Canada. Volunteers supported clients through goal setting, motivation, education and connections to community resources and primary care. This study aimed to create and apply a volunteer program evaluation framework to explore: (a) volunteer training effectiveness (learning online content, in-person training, self-efficacy in role tasks, training overall); (b) feasibility of program implementation (process measures, reflections on client encounters, understanding of volunteer roles/responsibilities, client perspectives on volunteer program); and (c) effects of volunteering on volunteers (health outcomes, self-efficacy, value of volunteering). A concurrent triangulation, mixed-methods design was used. Data were collected in 2016, sources included: volunteer online training quizzes, focus groups, self-efficacy survey, Veterans RAND 12-Item (VR-12) survey, in-person training feedback forms and narratives of client visits; client interviews; and quantitative implementation data. Quantitative data analysis included descriptive statistics, paired samples t tests, and effect size (Cohen's d). Qualitative data used descriptive thematic analysis. Nineteen volunteers and 12 clients participated in this evaluation. Findings demonstrate the volunteer program evaluation framework in action. Online training increased knowledge. In-person training received largely positive evaluations. Self-efficacy was high post-training and higher after volunteering. VR-12 sub-scale means increased descriptively. Volunteers understood themselves as healthcare system connectors, feeling fulfilled with their contributions and learning new skills. They identified barriers including not having the resources and skills of healthcare professionals. Clients found volunteers were a major program strength, appreciating their company and regular goals follow-up. Using a volunteer program evaluation framework generated rich and comprehensive data demonstrating the feasibility of bringing volunteers into primary 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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".