This research paper examines registered nurses' comfort with and use of computers as a barrier to online continuing education. Using the Staggers Nursing Computer Experience Questionnaire administered at two hospitals (N=172), the study analyzes Pearson product-moment correlation coefficients across five variables: age, gender, number of computer courses taken, self-assessed experience, usage, and knowledge. Results reveal statistically significant inverse relationships between age and both computer course-taking and experience, as well as a significant positive correlation between number of courses taken and self-assessed computer knowledge. Gender showed no significant relationship with usage or knowledge. Frequency data on self-reported obstacles to computer use are also presented, with computer anxiety and fear of losing information emerging as the most common barriers.
It is extremely important for the continued development of nursing that individuals employed within the profession make full use of available educational resources. Upon researching the most effective methods of continuing education that do not disrupt registered nurses' ability to work, it was observed that nurses were categorically not making use of online continuing education opportunities offered by their employers. Further, there was a general degree of distrust toward individuals who had obtained additional certifications through online courses.
In an effort to better understand the causes of this distrust, researchers distributed the Staggers Nursing Computer Experience Questionnaire at two hospitals. The goal was to determine, through the collection of first-hand data, nurses' general usage of, experience with, and comfort using computers across a range of categories — from the number of computer courses undertaken to the various ways participants interacted with computers on a daily basis.
The data collected were analyzed through a series of Pearson correlation studies to determine the degree of correlation, if any, among six significant variables: gender, age, experience, usage, courses, and knowledge. These variables were combined in every possible pairing and subjected to a two-tailed Pearson product-moment correlation coefficient analysis. A two-tailed test was chosen because the direction of any relationship was not known in advance.
The following descriptive statistics and correlation output summarize the relationship between participant age and number of computer courses taken (N = 172).
Descriptive Statistics: Age — Mean 2.78, SD 0.771; Courses — Mean 1.73, SD 1.511.
Correlations (Age Ă— Courses): Pearson r = .189*, Sig. (2-tailed) = .013; Sum of Squares and Cross-products = 37.616; Covariance = .220. The correlation is significant at the 0.05 level (2-tailed).
There is a statistically significant correlation between participant age and the number of computer courses taken. Because the age variable is coded 1–4, with 1 representing the oldest cohort and 4 the youngest, an increasing age-code value indicates a younger participant. As this value increases, participants are more likely to have taken more computer courses. It is therefore a reasonable assertion that the number of courses taken is inversely proportional to age: younger nurses have taken more computer courses than their older colleagues.
The following output summarizes the relationship between participant age and self-assessed computer experience (N = 172).
Descriptive Statistics: Age — Mean 2.78, SD 0.771; Experience — Mean 3.87, SD 1.463.
Correlations (Age Ă— Experience): Pearson r = .347**, Sig. (2-tailed) = .000; Sum of Squares and Cross-products = 66.919; Covariance = .391. The correlation is significant at the 0.01 level (2-tailed).
There is a statistically significant correlation between participant age and self-assessed degree of computer experience, both at work and at home. As the age-cohort code increases (1–4) and the experience scale increases (0–8), the two move together. It is therefore reasonable to infer that experience is inversely proportional to age: younger nurses report greater computer experience than older nurses. This finding is consistent with broader research on generational differences in technology adoption.
Age × Usage (N = 172): Age — Mean 2.78, SD 0.771; Usage — Mean 62.41, SD 15.492. Pearson r = .133, Sig. (2-tailed) = .081; Sum of Squares and Cross-products = 272.465; Covariance = 1.593.
There is no statistically significant correlation between participant age and self-assessed computer usage either at work or at home.
Age × Knowledge (N = 172): Age — Mean 2.78, SD 0.771; Knowledge — Mean 62.63, SD 15.682. Pearson r = .117, Sig. (2-tailed) = .128; Sum of Squares and Cross-products = 240.860; Covariance = 1.409.
There is no statistically significant correlation between participant age and self-assessed knowledge regarding the use of computers.
"No significant age correlations with usage or knowledge"
"Gender correlates with experience but sample is skewed"
"More courses linked to higher self-assessed knowledge"
"Anxiety and fear of data loss top reported barriers"
You’re 47% through this paper. Sign up to read the remaining 4 sections.
Sign Up Now — Instant Access Already a member? Log inAlways verify citation format against your institution’s current style guide requirements.