Understanding how volunteer companionship impacts those during the end of life: A realist evaluation
Bibliographic record
Abstract
Volunteers are a popular unpaid support role in end of life care yet how accompaniment influences the dying is underdeveloped.This study examined how companionship works, for whom, in what circumstances and why.Initial realist ideas were developed through participant observation (14 months), document analysis, and realist interviews with companionship trainers (n = 6).Theory testing involved volunteer interviews (n = 7), accounts from the dying, proxy accounts for the dying, and written reflections from companionship training.Companionship helps people live well until they die, prepare for death, and experience a good death.Four areas of volunteering explain these outcomes namely a loving friend, a holistic presence, a non-judgmental intermediary, and wrap around care.The four areas activate mechanisms related to reminiscing, preserving dignity/personhood, and easing suffering, contingent on specific contexts.The findings unpack how volunteering exerts its influence and what contextual factors facilitate outcomes, advancing the knowledge in this area.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".