140Keep On Volunteering: What Motivates Trained Mealtime Volunteers On Acute Medical Wards To Carry On
Bibliographic record
Abstract
Many people are involved in voluntary work within health and social care. Trained mealtime volunteers were introduced into Southampton General Hospital in 2011 and now help older people across all Medicine for Older People wards, the Acute Medical Unit, Orthopaedics and adult medical wards. However, mealtime volunteers are a costly investment that needs to be retained. The Volunteer Retention Questionnaire (VRQ) was designed for community-based palliative care volunteers in Canada, to examine their reasons for continuing to volunteer. This study aimed to adapt the VRQ for use with mealtime volunteers in NHS hospital wards, and then use it to examine why volunteers keep on volunteering. The original VRQ contained 33 reasons that may contribute to a decision to keep volunteering. We adapted the language used in the VRQ to British equivalents (e.g. ‘badge’ not ‘pin’) and two reasons specific to community care were removed. Participants rated the importance of the remaining 31 reasons using a 5-point Likert scale. Mean values and standard deviations were obtained for each reason. 24/41 (59%) volunteers participated. 18/24 (75%) participants completed the VRQ fully: missing data mainly reflected volunteers’ lack of personal experience of particular reasons. The highest rated reasons for continuing to volunteer were enjoyment of the role and feeling adequately trained (mean value for both reasons = 4.8, SD = 0.5). The lowest rated reasons were receiving phone calls/cards on special occasions (mean value = 1.6, SD = 1.0) and having the coordinator privately thank them for volunteering (mean value = 1.9, SD = 1.1). The VRQ was successfully adapted for UK volunteers. Despite differences in setting, patients and country, the results are remarkably consistent with the original VRQ. Volunteer coordinators seeking to promote retention should ensure volunteers receive the training required for them to enjoy their role and feel that their contribution is valuable to patients and staff.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".