Caring ability of nursing students pre- and post-internship: a longitudinal study
Bibliographic record
Abstract
BACKGROUND: Nursing students' internship experiences may significantly impact their caring ability. However, there is a lack of comprehensive evaluation of undergraduate nursing students' caring ability pre-and post-internship in China. This study aimed to explore the differences in the caring ability of undergraduate nursing students before and after internship. METHODS: The sample comprised 305 undergraduate nursing students who had undergone internships during 2018-2020 in three hospitals in Changsha, China. Caring Ability Inventory was used to measure and compare nursing students' caring ability before and after internship. Descriptive statistics and paired t-test were employed to analyze data in SPSS software (version 22.0). RESULTS: A total of 300 students completed the survey (response rate = 98.37%). The overall score of caring ability and scores of cognitive and patience dimensions were higher after internship than before internship (P < 0.05). There was no significant improvement in the courage dimension (P > 0.05). CONCLUSIONS: Caring ability of undergraduate nursing students in China was at a low level, their overall caring ability significantly improved after the internship, indicating a positive relationship between internship and caring ability. Nursing educators and clinical nurses should emphasize the importance of caring ability development in internship planning and encourage nursing students to engage more with patients.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".