Evaluation of Nursing Students’ Communication Abilities in Clinical Courses in Hospitals
Bibliographic record
Abstract
BACKGROUND: Joint Commission on Accreditation of Healthcare Organizations (JCAHO) has established, improving communication as a priority for improving patient safety since 2006. Therefore, the present study aimed to evaluate nursing students' communication abilities to recognize their strengths and weaknesses in communication skills. METHOD: This cross-sectional study was carried out in 2014. The study participants included all the nursing students who passed two semesters in Fatemeh School of Nursing and Midwifery in Shiraz, Iran. The students' communication skills were assessed using a self-administered questionnaire. Then, the data were entered into the SPSS statistical software (v. 16) and analyzed using both descriptive (mean and percentage) and inferential statistics (Pearson correlation and ANOVA). RESULTS: Among the 200 students who completed the questionnaires, 58% were female and 42% were male with the mean age of 21.79 years (SD=2.14). The results of Pearson correlation analysis demonstrated a significant correlation between the nursing students' clinical communication behavior scores and treatment communication ability scores (P<0.001). The findings demonstrated that most nursing students required improvement in their communication skills in both clinical communication behavior and treatment communication ability. Besides, a significant difference was observed among the students of different terms regarding clinical communication behaviors (P?0.05), but not concerning communication abilities. Nursing students in higher semesters had better communication skills. CONCLUSIONS: The results showed that nursing students in this university had a moderate ability in clinical and treatment communication. Thus, paying attention to standard education, curriculum revision, and adding some specific theoretical lessons for improving communication skills are mandatory during the bachelor's degree.
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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 | 0.002 | 0.011 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".