A Qualitative Study of Experiences, Motivations and Challenges for Volunteers in a Community-Based Palliative Care Program in a Lower-Middle Income Country
Bibliographic record
Abstract
Objectives: The aim of this study was to explore the experiences, motivations and challenges faced by palliative care volunteers in Bangladesh. Materials and Methods: Qualitative semi-structured interviews were conducted with volunteers in two community-based palliative care projects. Interviews were recorded, transcribed and coded using thematic analysis. Results: Ten volunteers provided insights into their experiences. The following major themes were identified, including motivation and impact, training and roles, challenges, support system, and future directions. Volunteers were highly motivated and developed deep and trusting relationships with the patients and family members. Volunteers identified the importance of receiving support from the palliative care team and other volunteers, to motivate and sustain them. Volunteers were active in addressing myths and barriers to acceptance of palliative care with patients and their families. The impact for volunteers extended beyond personal satisfaction, as they began to feel that palliative care was so important that it should be widely available and needed to be implemented nationally. Conclusion: Recruiting palliative care volunteers from the same community as patients may enhance palliative care programs by helping healthcare providers to understand the unique needs and perspectives of the local community. Integrating volunteers into the palliative care team provides them with support to cope and sustains them in their volunteer role. Incorporating the voices of volunteers into palliative care advocacy activities should be considered, as they bring a unique perspective on the importance of palliative care for their community and country.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".