How to Attract More Males to Community-Based Hospice Palliative Care Volunteer Programs
Bibliographic record
Abstract
Two separate studies were conducted to better understand why so few middle-aged and older men volunteer in hospice palliative care; only about 10% of the patient/family care volunteers in New Brunswick's community-based hospice palliative care volunteer programs are men. In study 1, 15 (22%) of the 68 men who read a brief description about the kinds of things that hospice palliative care volunteers do expressed an interest in this type of volunteerism. The main reasons given for their lack of interest included ''being too busy'' and ''not being able to handle it emotionally.'' At least one third of the men who said ''No'' to becoming a hospice palliative care volunteer expressed an interest in 10 of 13 other common volunteer activities (eg, driving). In study 2, 59 men were presented with a list of 25 tasks that hospice palliative care volunteers might perform when providing emotional, social, practical, and administrative support. The men were asked to indicate which tasks they would be willing to perform if they were a hospice palliative care volunteer. The men were least willing to serve on the board of directors (28%), provide hands on patient care (38%), and work in the volunteer program's office (42%); they were most willing to talk to the patient (97%), share hobbies and interests with the patient (92%), listen to the patient's memories and life stories (90%), and provide friendship and companionship (88%). The results of these studies may have implications for the recruitment of male volunteers to work with dying patients and their families.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".