Effect of Nursing Intervention on Emotional Intelligence, Self-esteem and Empathy of Nursing Students Undergoing Clinical Experience in Paediatric Units-Pilot Study (Part-2)
Bibliographic record
Abstract
Background: Nurses working in paediatric settings face a number of challenges. Nurses can be emotionally drained while they see their young patients suffer. Therefore, nurses need to be emotionally intelligent. Objectives: The effect of nursing intervention on emotional intelligence (EI), self-esteem and empathy of baccalaureate nursing students undergoing clinical experience in paediatric units. Materials and Methods: The present study adopted a quasi experimental repeated measures time series design in which 60 third year baccalaureate nursing students undergoing clinical experience in paediatric units were assigned to either the experimental or control groups. Pre testing of emotional intelligence, self-esteem and empathy was done using the EI (Pc-Sc) scale, Rosenberg self-esteem scale and the Toronto Empathy Questionnaire, respectively. The study intervention included a nursing intervention programme offered in 5 two-hour sessions for 5 days. Post-test was conducted on the 30th and 60th day. Statistical Analysis: Two factor repeated measures ANOVA was used for comparison within the group and between the groups. Results: In the experimental group, the mean and standard deviation of the different areas of EI and overall EI score, self-esteem and empathy at post test 2 were higher than the mean and standard deviation at pretest. There was a significant change in the EI, self-esteem and empathy scores in the follow ups in the experimental group, indicating that the nursing intervention was effective in enhancing the EI, self-esteem and empathy of baccalaureate nursing students. Conclusion: Courses to train the emotional intelligence skills, self-esteem and empathy should be included in the nursing curriculum so as to improve nursing standards of both students and nurses in providing quality care to the patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".