Bibliographic record
Abstract
Since Florence Nightingale, spiritual care has been embraced in professional nursing as the integration and promotion of purpose and meaning in life. Research reports that nurses have a prominent regard for spiritual care and acknowledge that it is part of their role. The intention of this literature review is to explore effective ways for nursing faculty to teach spiritual care. This in turn will help to advance spiritually educated nurses who can rise above obstacles to providing spiritual care to clients. During the nursing formation process, spiritual care must be integrated into the nursing curricula throughout the didactic and clinical nursing education. Numerous nurses today consider themselves underprepared to meet the spiritual needs of their clients, despite nursing’s past dedication to spiritual care. Research further supports the appeal of clients to have their spiritual needs addressed while hospitalized. Given the suitable resources and education, nurses can be positioned to address the spiritual care of clients. Relevant nursing education on more effective ways to teach spiritual care will enhance the delivery of this vital aspect of holistic nursing. The focus of holistic patient care is based on the Neuman systems model, where the spiritual variable considerations are necessary for a truly holistic perspective and caring concern for the patient. As part of a successful pedagogy, a course of action and theory-based approach must be translated into preparing student nurses to incorporate spiritual care into their emerging practices.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".