Maximizing Ageing Using Volunteer Engagement (MAUVE): one health system’s journey to spread a volunteer-based intervention for acutely ill older adults – a prospective observational study
Bibliographic record
Abstract
Objectives Older inpatients face a higher risk of delirium, falls and functional decline during hospital stays. Volunteer programmes have been shown to improve patient outcomes in single settings, but little is known about their implementation and spread across multiple care environments. This study describes the implementation and system-wide spread of Maximizing Ageing Using Volunteer Engagement (MAUVE) —a volunteer-based programme supporting older patients’ cognitive, physical and social well-being—and evaluates its impact on healthcare staff satisfaction. Design A prospective observational service evaluation. Setting Emergency department, seven acute in-patient care units and two transitional care units within a Canadian hospital system from January to December 2019. Participants Older patients receiving care, volunteers delivering interventions and front-line nursing staff. Interventions Trained volunteers delivered up to six types of interventions targeting patients’ cognitive stimulation, physical activity, social engagement, functional support, orientation and companionship. Outcome measures Staff satisfaction with the MAUVE programme was measured using a structured survey administered 6 months after programme implementation. Data on patients and volunteers—including the number and type of interventions delivered, volunteer hours and patient reach—were also collected to assess feasibility and programme uptake. Results Over 12 months, 94 volunteers delivered 31 593 interventions to 3568 unique patients across three care settings. Front-line staff reported high satisfaction with the MAUVE programme, noting that volunteers enhanced patient care and enabled more direct patient interaction by staff. Conclusions The MAUVE programme is the first known volunteer-led patient engagement programme to be successfully implemented across acute, emergency and transitional care settings. This service evaluation demonstrates that structured volunteer engagement can support older patients’ well-being while enhancing staff satisfaction and enabling front-line care providers to deliver more direct care.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".