A Few Good Men: It’s Not Easy Recruiting Male Hospice Palliative Care Volunteers
Bibliographic record
Abstract
Two studies were conducted to explore how to engage male volunteers in hospice palliative care. Four male hospice palliative care volunteers were interviewed in study 1. The men agreed that a direct approach is best when it comes to recruiting male volunteers, especially a personal story or testimonial. Two different volunteer position descriptions were created for study 2: one description was similar to what might appear on a community-based hospice palliative care program's web site or in a newspaper ad looking for visiting hospice palliative care volunteers; the other description was in the form of a personal testimonial ostensibly written by a male hospice palliative care volunteer describing his role through examples of interactions he has had with patients and patients' family members. Twenty-five males responded to each description. Both of the descriptions generated low and nonsignificantly different levels of interest in becoming a hospice palliative care volunteer. Believing this work to be too emotionally demanding and not having enough time for volunteering were the two most commonly given reasons for not wanting to become a hospice palliative care volunteer. Suggestions for future recruitment efforts are discussed.
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 | 0.041 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".