Volunteer Programs for Hospitalized Older Adults in North America, Europe, and Australia: A Scoping Review
Bibliographic record
Abstract
Due to the rapidly aging population, hospitals are increasingly caring for more older patient. Implementing volunteer programs focused on providing care to hospitalized older adults is a way for hospitals to better support them. Despite the growing presence of volunteer programs in hospital settings, there remains a limited understanding of how these programs are structured, their impact on patient care, and their integration within healthcare teams. Addressing this gap is essential for optimizing volunteer engagement and improving hospital-based support for older adults. The purpose of this scoping review is to assess the existing literature surrounding volunteer programs designed to support hospitalized older adults and identify gaps that are present for future research and inquiry. Employing Arskey and O'Malley's scoping review methodology, 27 publications met our study's inclusion criteria. Thematically analyzing our data surfaced three overall themes: (1) the Influence of Volunteer Training on Roles and Functions, (2) Volunteer Perspectives on their Roles, and (3) the Impact of Volunteers in Hospital Settings. We noted that volunteers have different motivations for participating in volunteer programs. Healthcare professionals generally have positive views of hospital volunteer programs for older adults, but also express reservations and tend to have limited interactions with volunteers. Moreover, as volunteer roles were seen mainly to supplement family caregiver roles, future volunteer programs are encouraged to also consider the unique roles and needs of families and develop solutions to ensure that quality care can be delivered to both older patients and their family caregivers. Future research should explore how volunteer programs can be better integrated within interdisciplinary teams, assess their long-term impact on patient outcomes, and identify strategies to strengthen collaboration between volunteers, healthcare professionals, and family caregivers to optimize care for hospitalized older adults.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".