Volunteers as members of the stroke rehabilitation team: a qualitative case study
Bibliographic record
Abstract
OBJECTIVES: Clinicians are facing increasing demands on their time, exacerbated by fiscal constraints and increasing patient complexity. Volunteers are an essential part of the many healthcare systems, and are one resource to support improved patient experience and a mechanism through which to address unmet needs. Hospitals rely on volunteers for a variety of tasks and services, but there are varying perceptions about volunteers' place within the healthcare team. This study aimed to understand the role of volunteers in stroke rehabilitation, as well as the barriers to volunteer engagement. DESIGN: A qualitative case study was conducted to understand the engagement of volunteers in stroke rehabilitation services within a complex rehabilitation and continuing care hospital in Ontario, Canada. PARTICIPANTS: 28 clinicians, 10 hospital administrators and 22 volunteers participated in concurrent focus groups and interviews. Organisational documents pertaining to volunteer management were retrieved and analysed. RESULTS: While there was support for volunteer engagement, with a wide range of potential activities for volunteers, several barriers to volunteer engagement were identified. These barriers relate to paid workforce/unionisation, patient safety and confidentiality, volunteer attendance and lack of collaboration between clinical and volunteer resource departments. CONCLUSIONS: An interprofessional approach, specifically emphasising and addressing issues related to key role clarity, may mediate these barriers. Clarity regarding the role of volunteers in hospital settings could support workforce planning and administration.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".