Integrating Spiritual Care into a Baccalaureate Nursing Program in Mainland China
Bibliographic record
Abstract
Holistic nursing care takes into account individual, family, community and population well-being. At the level of individual well-being, the nurse considers biological, psychological, social, and spiritual factors. However, in Mainland China spiritual factors are not well understood by nursing students. And accordingly, nursing faculty and students are reluctant to broach the topic of spirituality because it is either unknown to students or students believe that the provision of spiritual care is beyond their capabilities. We wonder then, what can we do as nurse educators to integrate spiritual care into a baccalaureate nursing program in Mainland China? The purpose of this article is to propose the integration of Chinese sociocultural traditions (namely religious/spiritual practices) into undergraduate nursing curricula as a means to enter into dialogue about spiritual well-being, to promote spiritual care; and to fulfill the requirements of holistic nursing care. However, prior to discussing recommendations, an overview of the cultural context is in order. Thus, this article is constructed as follows: first, the complexity of Chinese society is briefly described; second, the historical evolution of nursing education in Mainland China is presented; and, third, strategies to integrate Chinese religious/spiritual practices into curricula are proposed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".