The concept of hope in nursing 3: hope and palliative care nursing
Bibliographic record
Abstract
This article is the third in a series of six that explores the nature of hope, reviews the existing theoretical and empirical work in several discrete areas of nursing, and provides case studies to illustrate the role that hope plays in clinical situations. This article focuses on hope within the specialty area of palliative care nursing. Nurse researchers have been instrumental in our current early understandings of hope in palliative care. Studies of hope in palliative care, over the past decades, have focused primarily on those individuals in the advanced stages of cancer and the human immunodeficiency virus. Studies using quantitative methodology have focused on exploring hope levels across the dying trajectory and the relationship between hope and other psychosocial variables while those using qualitative methodology have focused on the meaning of hope and elucidating how terminally ill individual maintain and engender their hope. Research supports that the clinician is an instrument through which hope can be assessed and administered. There is a need for further rigorous investigation of the role of hope during the terminal phase of an illness with specific emphasis on capturing the intangible inner experiences of hope and on the validation of interventions/strategies that develop and maintain hope for both the terminally ill person and his/her family caregivers and significant others.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".