Caring competencies of baccalaureate nursing students of samar state university
Bibliographic record
Abstract
Background: Caring is a core nursing value and a desirable attribute in nursing students. This study was conducted to investigate and determine student nurses’ caring competencies as perceived by patients. Furthermore, it proposed a caring intervention guide which could be used to build and enhance student’s caring behaviors that can be adapted to clinical situations. Method s: This investigation primarily employed a descriptive research design to determine the caring competencies of Level IV students of Samar State University as perceived by the patients. A non-probability purposive sampling was utilized in this study. A total of 174 patients who were admitted in the different units of the hospital were taken as respondents. To examine the caring competencies of nursing students, the investigator utilized the modified Cronin and Harrison’s Caring Behavior Assessment Tool. This tool is a 63-item questionnaire that uses a 5-point Likert scale and is based on Watson’s ten Carative Factors and is designed to capture patients’ perceptions of nurses’ caring behaviors. Results: Results showed that “Know how to give shots, IVs, etc”, “Kind and considerate”, “Help me feel good about myself”, and “Give me treatments and medications on time”, were the highest ranked caring behaviors of student nurses. The subscale “human needs assistance” was rated as highest by patient respondents. The results generated the caring intervention guide that would assist nurse educators to improve students’ caring practices. Conclusion: Findings give high regards to Jean Watson’s assumptions that caring is demonstrated by using actions based on theoretical knowledge that help patient to achieve health while maintaining respect, self-worth and autonomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".