Establishment of a Geriatric Nursing Curriculum with Human Caring by Situational Simulation
Bibliographic record
Abstract
It is generally accepted that human caring is the essence of nursing work and the foundation of nursing quality. However, nurses’ human caring ability does not currently adapt well to the needs of patients and hospital work. The main purpose of this study is to build a humanistic care geriatric nursing course in order to enhance the humanistic care ability of nursing students, thereby improving patient satisfaction and service quality. An expert panel with 12 academics and scholars was applied as a method to review the course syllabus. The result presented that the curriculum contains eight common diseases and problems of the elderly are selected, and nursing positions are integrated into situational tasks with caring factors. Love is taken as the essence of the roles of Standardized Patients, nurses, doctors, teachers and students to design a transpersonal care field constructed of loving interactions to realize transpersonal human caring. Twenty-two situations are created with the characteristics of typical simulated situation tasks to integrate human caring into a geriatric nursing curriculum. The establishment of a geriatric nursing curriculum that includes human caring by simulating certain situations would be effectively applied to enhance students’ human caring ability in nursing vocational College.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".