Views on death and dying among health care workers in an Indian cancer care hospice: Balancing individual and collective perspectives
Bibliographic record
Abstract
In providing palliative and end-of-life care, professional and lay hospice workers alike attend to patient and family needs to encourage a dignified death. However, there are few comparative inquiries documenting how differential workplace preparation affects the processes and outcomes related to being confronted to death and dying. This qualitative study explores and compares these experiences among a diverse sample of health workers (N = 25) in a grassroots cancer care hospice in Bangalore, India. Our findings underscore how personal views, socio-economic status, beliefs and values, occupational experience, and workplace interventions interact to shape 'worldviews' about death and dying. Whereas health workers report conflicting feelings of relief and sadness when confronted with the death of their patients, these mixed emotions are often lessened through open dialogue among newly trained and more experienced health workers. Moreover, experienced hospice workers wished to ensure that less experienced ones are provided with the necessary workplace support to lessen psychological 'hardening' that may occur with repeated exposure to death. In dealing with the diverse needs of hospice workers, both individual and collective needs must be considered to ensure an optimal workplace climate. Future work should study how hospice workers' views on death and dying evolve with time and experience.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".