Supporting hospice volunteers and caregivers through community-based participatory research
Bibliographic record
Abstract
Drawing on the results of community-based research with a local hospice organisation, this article addresses the need to enhance social support for caregivers of people with life-threatening illnesses. The goal of the research was to involve palliative care stakeholders in the identification, prioritisation and implementation of social support interventions for caregivers who provide palliative care support as hospice volunteers and as family members of those at end-of-life. Guided by a community-based participatory research approach, primary data were collected from 39 volunteer and family member caregivers through four focus groups and nine personal diaries in July 2008. Content analysis and modified constant comparison techniques resulted in emergent themes and priorities relating to challenges, existing coping strategies and resources, and potential support interventions. The findings revealed communication, emotional support, education, advocacy and personal fatigue as the most important challenges to be addressed through support interventions at the organisational (professional support, volunteer mentoring and continuing education) and household levels (caregiver assessments, telephone support and follow-up). There was convergence in how caregivers perceived and access existing social supports, yet a crucial divergence in the availability of resources among volunteers and family members. The findings are discussed in the light of the capacity for hospices to implement social supports and the potential efficacy of the community-based participatory research approach for enhancing social support for caregivers in other parts of health-care and social 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 | 0.103 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".