End-of-life care volunteers: a systematic review of the literature
Bibliographic record
Abstract
This report presents a review of 1988 and onwards research and other literature on end-of-life (EOL) care volunteers. Only 18 research or case studies articles were identified for an integrative review through a search of nine library databases. A review of this literature revealed three themes: (1) the roles of EOL volunteers, (2) volunteer training and other organizational needs or requirements, and (3) outcomes, particularly the impact of volunteering on volunteers and the impact of volunteers on EOL care. Despite limited statistical evidence, the available literature on EOL care volunteers clearly indicates that considerable potential benefit can be derived from EOL care volunteers' contributions, with their efforts benefiting dying persons, their families, paid EOL staff, and the volunteers themselves. More specifically, willing volunteers, particularly those with diverse skills and abilities, have the potential to significantly and positively impact EOL care in that they can perform many necessary and extra functions of value. Volunteers often augment and enhance the range of EOL care services provided to terminally ill individuals and their families. Volunteers should also be recognized as increasing the accessibility of EOL care. The role of the volunteer is not without challenge, however, both for the individuals who volunteer and the organizations that must orient them and provide a meaningful role for them.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".