End-of-life conversations about death and dying from volunteer perspectives: A qualitative study
Bibliographic record
Abstract
OBJECTIVES: Although often unrecognized, volunteers fulfill many essential roles in hospices and other end-of-life care settings. Volunteers complement the actions of professionals in fulfilling many extra care needs, such as delivering newspapers and tidying bedsides. We explored end-of-life conversations about death and dying between hospice volunteers and terminally ill people, with a particular emphasis on any expressed desire to die. Our 2 research questions were as follows: (1) What is the nature of end-of-life conversations between hospice patients and hospice volunteers? and (2) How do hospice volunteers experience conversations about death and dying with patients who are at the end-of-life? METHODS: We conducted semi-structured interviews using an interpretive phenomenological analysis. We recruited hospice volunteers from 4 hospices in Calgary, Edmonton, and Red Deer; 3 larger cities in the province of Alberta, Canada. RESULTS: We interviewed 12 participants to saturation. Four themes emerged: (1) trusting conversations about death and dying in the context of a safe place; (2) normalcy of conversations about death and dying; (3) building meaningful relationships; and (4) end-of-life conversations as a transformative experience. Our results emphasize the importance of preparing volunteers for conversations about death and dying, including the desire to die. SIGNIFICANCE OF RESULTS: The safe environment of the hospice, the commitment to patient confidentiality, and the ability of volunteers to meet the basic and emotional needs of dying people or simply just be present without having formal care duties that need to be completed contribute to volunteers being able to participate in timely and needed conversations about death and dying, including the desire to die. In turn, hospice experiences and end-of-life conversations provide a transformative experience for volunteers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".