Volunteers’ experiences building relationships with long-term care residents who have advanced dementia
Bibliographic record
Abstract
Healthcare volunteers make important contributions within healthcare settings, including long-term care. Although some studies conducted in long-term care have shown that volunteers contribute positively to the lives of people living with advanced dementia, others have raised questions about the potential for increasing volunteers’ involvement. The purpose of this study is to understand volunteers’ perspectives on their work and relationships with long-term care residents with advanced dementia. A total of 16 volunteers participated in semi-structured interviews about their experiences. Interview data were analyzed using an inductive approach to thematic analysis. In this analysis, a central concept, relationships in dementia care volunteering, enveloped four related themes: mutuality and empathy as the foundation for dementia care relationships with residents, family as the focus of volunteer relationships, relationships shaped by grief, and staff support for volunteer relationships. We conclude that in long-term care settings, volunteer roles and relationship networks are more robust than they are often imagined to be. We recommend that long-term care providers looking to engage volunteers consider training and supporting volunteers to cultivate relationships with residents, family, and staff; navigate experiences of loss; and be considered as members of dementia care teams.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".