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The relation between nursing students' levels of self-efficacy and caring nurse-patient interaction: a descriptive study.

Handan ErenAyse Sonay Türkmen
Published in: Contemporary nurse (2020)
Background: Nursing is a process of interpersonal interaction. Objective: To determine the relationship between the self-efficacy levels and caring nurse-patient interactions of nursing students. Methods: The study was carried out with 198 students who agreed to participate. The data was collected by using personal information forms, the Caring Nurse-Patient Interaction Scale (CNPIS), and the Self-Efficacy-Sufficiency Scale (SES). Collected data were evaluated via descriptive statistics, independent group t-test, ANOVA, and Tukey HSD and Pearson Correlation analysis. Results: CNPIS sub-dimension point averages were 309.94 ± 34.71 for importance, 278.97 ± 40.81 for competency, and 264.04 ± 46.17 for feasibility. It was determined that there are statistically significant, strong, and positive relations between all sub-dimension of the scale. It was also determined that the scale sub-dimension point averages differ in a statistically significant way on the basis of certain demographic attributes (sex and age of the students (p<0.05)). Furthermore, a statistically significant, positive and medium level relation was determined between the CNPIS importance and competence point averages and SES total points (rimportance: 0.318, pimportance: 0.000; rcompetence: 0.322, pcompetence: 0.000) Conclusions: It was concluded that students scored high points from all sub-dimensions of the CNPIS. It was further determined that students with high levels of self-efficacy/sufficiency have higher levels of caring for nurse-patient interaction. Impact statement: Adequate self-efficacy and sufficiency will be able to provide better nurse-patient interaction which based on care.
Keyphrases
  • case report
  • nursing students
  • primary care
  • high school
  • mental health
  • palliative care
  • electronic health record
  • big data
  • quality improvement
  • deep learning
  • social media
  • cross sectional
  • data analysis