Softening Suffering Through Spiritual Care Practices: One Possibility for Healing Families
Bibliographic record
Abstract
Nurses are engaged and encounter suffering routinely and commonly in their everyday practice. It is therefore a moral and ethical obligation for nurses to soften the emotional, physical, and spiritual suffering of the individuals and families in their care. Softening suffering is the heart of nursing. However, this article ponders the question, "What happened to suffering in nursing care?" A discussion of suffering is explored from many aspects, such as what invites suffering and the connection of suffering to spirituality. Lessons learned from the author's clinical practice and research are described, such as acknowledging suffering, social support, hope and prayer, and individual and family counseling. Finally, seven spiritual care practices within the Trinity Model that have shown to be useful in softening suffering are offered. An actual clinical example is woven throughout to illustrate the benefits of these spiritual care practices in the mission of softening illness suffering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".