The Inventory of Motivations for Hospice Palliative Care Volunteerism: A Tool for Recruitment and Retention
Bibliographic record
Abstract
Given the essential role of volunteers in hospice palliative care, it would be beneficial to have a recruitment and retention tool that is reliable and valid. To address this gap, the current investigation sought to adapt and extend the Inventory of Motivations for Palliative Care Volunteerism (IMPCV) of Claxton-Oldfield, Jefferies, Fawcett, Wasylkiw, and Claxton-Oldfield.(1) The purpose of study 1 was to address methodological concerns of the IMPCV using 141 undergraduate students. After conceptually relevant items were added to the IMPCV, participants indicated the degree of influence each of the motivations would have on their, and another person's, decision to become a hospice palliative care volunteer. In both cases, 5 internally consistent subscales were identified through principal components analysis: altruism, civic responsibility, self-promotion, leisure, and personal gain. Convergent and discriminant validity were demonstrated using an established measure of empathy. In study 2, 141 hospice palliative care volunteers completed the revised and renamed Inventory of Motivations for Hospice Palliative Care Volunteerism (IMHPCV). Confirmatory factor analysis provided support for the 5-factor structure of the IMHPCV. The authors encourage other researchers to use the IMHPCV as a measurement tool in studying the motivations of hospice palliative care volunteers.
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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 | 0.037 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".