The Feasibility of Creating Partnerships Between Palliative Care Volunteers and Healthcare Providers to Support Rural Frail Older Adults and Their Families: An Integrative Review
Bibliographic record
Abstract
Background/Question: Volunteers are important in the support of frail older adults requiring palliative care, especially in rural areas. However, there are challenges associated with volunteer supports related to training, management and capacity to work in partnership with healthcare providers (HCP). This review addresses the question: What is the feasibility of a volunteer-HCP partnership to support frail older adults residing in rural areas, as they require palliative care? METHODS: This integrative review identified ten articles that met the identified search criteria. Articles were appraised using the Critical Appraisal Skills Programme (CASP) checklists, designed for use across a range of quantitative and qualitative studies. RESULTS: Studies were drawn from international sources to understand how volunteer roles vary by culture and organization; the majority of studies were conducted in North America. Studies varied in methodology, including quantitative, qualitative and educational commentary. Identified factors that were crucial to the feasibility of volunteer-HCP partnerships in rural areas included volunteer training dynamics, relationships between volunteers and HCP, and rural environmental factors. CONCLUSION: Preliminary evidence indicates that a volunteer-HCP palliative partnership is feasible. However, training policies/procedures, volunteer-HCP relationships, and rural specific designs impact the feasibility of this partnership. Additional research is needed to further establish the feasibility of implementing these partnerships in rural settings.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".