Pastoral Care in End-of-Life: Can you be Healed when there is No Cure?
Bibliographic record
Abstract
When death is coming for sure, the terminally ill patients will be in sorrow. For them who do not have hope of being medically cured, do having joy and feeling serene become a possibility? Can they be healed at some point? This research aims to describe the existential experiences of the terminally illness patients’ family receiving a pastoral care. While the patients deteriorate because of illness, what type and kind of care they found most helpful? This research was a qualitative study with a phenomenology method. This paper is part of a bigger case study conducted in a Catholic hospital in Indonesia. The subjects for this research were the families of terminally ill patients. They were interviewed whether they have received pastoral care and the significance of it. The presence of pastoral care staffs and their visits were significant for the families and the patients. They feel strengthened in facing this difficult times. In particular, the spirit of the patients uplifted, they no longer feel angry with their condition and are in process of accepting the death coming their way. They found prayers as powerful resources. All the participants agreed that the pastoral care is important part for the patients’ healing. Catholic hospitals in Indonesia should be aware to provide a holistic healing. Pastoral care is one of key factors to maintain the spirit of institutions. It is the way to humanize the patients and families in their end-of-life 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".