OA8 Caring for the family caregiver: working with volunteers to implement and improve a service to enable family caregivers to maintain their own wellbeing
Bibliographic record
Abstract
BACKGROUND: Family caregivers suffer physically, mentally, and spiritually. Community volunteers play an important role in supporting patients at the end of life or former caregivers in bereavement. However, there are no research reports of volunteer services focused on maintaining the wellbeing of end-of-life caregivers. AIM: To have volunteers, a hired volunteer coordinator, health care providers, and researchers implement and formatively evaluate a volunteer service to enable family caregivers to maintain their well being while providing care and subsequent bereavement. This presentation will focus on the volunteers' roles with the project as both agents of change to the service and as support for the caregivers. METHOD: A qualitative formative evaluation informed by Guba and Lincoln's Fourth Generation Evaluation (1989) participatory design was conducted. Data was collected through individual interviews, focus groups, participant observation during volunteer support meetings, and through volunteers' written reflections. RESULTS: Amongst the volunteers, volunteer coordinator, and principal investigator, there was mutual respect for and interest in learning about everyone's roles and experiences in the project. The experience was rewarding because they felt they helped the family caregiver and enjoyed developing and improving the service and working in a supportive team. Volunteers' challenges included being nervous for their first meeting with a caregiver, and frustration with some rules put in place to protect them (e.g. not helping the caregiver with direct care for the patient). CONCLUSION: Volunteers can be an effective part of the research team, while providing valuable support and encouragement for family caregivers to maintain their own wellbeing.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".