Canadian Journal of Nursing Informatics

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This article was written on 22 Sep 2024, and is filled under Current Issue, Volume 19 2024, Volume 19 No 3.

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Cyberphobia to Idealism: Student Attitudes Toward Healthcare Technology and Engagement in Online Learning

by Pinar Ozmizrak, MS, PhD

Luigi Boccuto, MD

Tracy Brock Lowe, RN, MS, PhD

Jane DeLuca, RN, MS, PhD, CPNP-PC

Clemson University School of Nursing, Clemson, SC 29634, USA

Citation: Ozmizrak, P., Boccuto., L., Brock Lowe, T., & DeLuca, J. (2024). Cyberphobia to idealism: Student attitudes toward healthcare technology and engagement in online learning. Canadian Journal of Nursing Informatics, 19(3).  https://cjni.net/journal/?p=13469

Cyberphobia to Idealism

Abstract

Since the COVID-19 pandemic, nursing education has incorporated more digital elements, and recent students have participated in online or hybrid courses. Previous research shows that undergraduate nursing students in a hybrid online US university healthcare science course strongly support the use of computers and technology in healthcare through high scores on the Pretest for Attitudes Toward Computers in Healthcare (PATCH) assessment scale (Kaminski, 2011). Research also shows that nursing students engage with online learning activities through interest, perceived helpfulness in understanding the material, and relevance to future nursing practice.

This correlational analysis sought to determine if students with a higher affinity for technology, measured by higher PATCH scores, would rate their engagement with online learning activities more highly. Among the class of N = 118 nursing students, those who scored the highest level on the PATCH scale—level 6, an “idealistic view” of computers in healthcare—rated activities on average significantly higher than students who scored level 5, an “enthusiastic view” (p=0.012) and level 4, a “realistic view” (p=0.006). The coefficient of determination (r2 = 0.91) revealed a strong positive relationship of PATCH level contributing to engagement rating. Overall, the difference in engagement with healthcare-focused online learning activities is highly attributable to students’ affinity for technology, as measured by the PATCH scale.

Background

From electronic medical records to smartphone apps to treatment delivery, nurses use increasing amounts of technology in practice when caring for people’s health. The integration of technology into care has important practical as well as philosophical implications for empowering nurses when faced with uses of technology to meet greater demands for efficiency and cost savings (Barnard, 2016). Over time, nurses will face more challenges with additional technological developments such as AI and robotic enhancements in care delivery (Mansour & Nogues, 2022).

Understanding and predicting nurses’ affinity and use of technology is a longstanding topic of interest among nurses, nurse researchers, nurse educators, and corporate entities (Barchielli, et al., 2021, Dykes & Chu, 2021; Strudwick, 2015). In this correlational analysis, the data sets from a hybrid online undergraduate nursing class on attitudes towards technology in healthcare (Ozmizrak et al., 2023) and engagement with online learning activities (Ozmizrak, 2023) were explored for potential relationships between nursing students’ affinity for technology in healthcare and their engagement in online learning.

Methods

Previously, US university nursing students’ perspectives towards the use of technology in healthcare were evaluated using the Pretest for Attitudes Toward Computers in Healthcare (PATCH) assessment scale (Kaminski, 2011; Ozmizrak et al., 2023). The PATCH results in that study revealed that nursing students taking a hybrid online healthcare science course had the highest scores among reported studies of the PATCH scale administered to other nursing undergraduates (Alaban et al., 2020; Atay et al., 2014; Vijayalakshmi et al., 2014). “With this study demonstrating that nursing students show support for technology in healthcare, future research can investigate if modernizing educational delivery through interactive digital coursework similarly achieves improvements in student engagement,” (Ozmizrak et al., 2023, p. 20). Results data from this PATCH study was used to assess students’ preferences for incorporating technology in nursing and their affinity for online class activities. 

The 118 nursing students’ distribution of PATCH level scores were as follows:

  • Level 1 – 0 students
  • Level 2 – 0 students
  • Level 3 – 1 student
  • Level 4 – 28 students
  • Level 5 – 53 students
  • Level 6 – 36 students

The higher the level of the score on the PATCH scale, the more supportive students were of technology use in healthcare. Scoring within levels 1, 2, and 3 indicated cyberphobia, unease, or limited awareness of the use of computers in healthcare, respectively. At the other end of the scale, those scoring level 4 were more likely to be comfortable with using computers and had a “realistic view” of their use in healthcare. Those scoring level 5 had both comfort and confidence in using computers and an “enthusiastic view” of them while level 6 indicated all of comfort, confidence, and creativity in computer use coupled with an “idealistic view” of computers in healthcare. The mode of the class measures was level 5. There was one outlier scoring level 3, with the rest of the class ranging from level 4 to 6.

The PATCH scale provided students’ ratings of their perspectives on technology use in healthcare with higher PATCH level scores presuming a higher level of support of technology.

Data was also collected from a mixed methods study conducted to examine the integration of online activities (interactive digital coursework) into a hybrid online healthcare science course for nursing students (Ozmizrak, 2023). Students rated their levels of engagement for eight unique online educational activities, described in Table 1. Student scores reflected their interest in the healthcare science topics, rating the helpfulness of the activity for achieving higher grades in the course, and the relevance of the activities to future nursing practice.

Table 1.

Interdisciplinary online healthcare activities given biweekly in the hybrid online nursing course.

From the results of the two studies, it was hypothesized that students who were supportive of computers and technology in healthcare may have higher ratings of engagement with online learning activities. The aim of this study was to examine the relationships between PATCH scores and student engagement with online learning activities.

H0 (null hypothesis) = Students with higher PATCH level scores did not rate online healthcare science activities higher on average

H1 (alternative hypothesis) = Students with higher PATCH level scores rated online healthcare science activities higher on average

Statistics used in the analysis included one-way analysis of variance (ANOVA), calculation of the coefficient of determination, and two-sample assuming unequal variances t-tests. The threshold for significance was p < 0.05.

Results

The average ratings of online healthcare science activities for each PATCH level group (Table 2) were determined by the combined average engagement ratings for interest, helpfulness, and relevance for each online learning activity, and across the eight activities combined. Groups for levels 1 and 2 were removed because no PATCH scores were captured for those levels. Each student rated each activity on the three factors (interest, helpfulness, and relevance) on a 5-point scale from 1 (lowest rating) to 5 (highest rating).  In total, students rated eight online activities with 24 questions total on a 5-point scale, which were averaged for each group.

Table 2.

Average rating of online healthcare science activities by PATCH level group.

Average rating of online healthcare science activities by PATCH level group.

ANOVA between the groups scoring level 4, level 5, and level 6 on the PATCH scale revealed a statistically significant difference (p = 0.009) between the groups’ average ratings of the online learning activities. Knowing that the groups were significantly different, the next step of analysis was to calculate the coefficient of determination with PATCH level as the independent variable and engagement rating as the dependent variable. The result was r2 = 0.91, revealing a strong positive relationship, accounting for 91% of the difference between average online healthcare science activity scores as attributable to PATCH level scores. As a positive relationship, with the increase of PATCH level score, average online healthcare science activity ratings also increased. Because of this positive relationship, the next tests of two-sample assuming unequal variances t-tests were analyzed by p one-tail rather than p two-tail (Table 3). Unequal variance was assumed rather than equal variance for a more conservative analysis.

Table 3.

Two-sample assuming unequal variances t-tests between PATCH group levels for combined activity average ratings (significant differences of p < 0.05 highlighted in yellow).

Two-sample assuming unequal variances t-tests between PATCH group levels for combined activity average ratings

The t-tests revealed no significant difference in average ratings of online healthcare science activities between groups of students who scored level 4 and level 5 on the PATCH scale (p = 0.215). However, there was a significant difference between both levels 4 and 6 (p = 0.006) and levels 5 and 6 (p = 0.012). Students who scored level 6 on the PATCH scale rated online healthcare science activities significantly higher than students who scored lower on the PATCH, rejecting the null hypothesis, H0.

There was a statistically significant difference by PATCH level when analyzing the combined data of the eight activities. The following table, Table 4, shows the results of two-sample assuming unequal variances t-tests with p one-tail between the PATCH groups for each activity individually.

Table 4.

Two-sample assuming unequal variances t-tests between PATCH group levels for individual activity average ratings (significant differences of p < 0.05 highlighted in color, with each color signifying a unique pattern).

Two-sample assuming unequal variances t-tests between PATCH group levels for individual activity average ratings

For activities 1, 2, 4, 6, and 8, the average rating of the online learning activity increased in the order of level 4, level 5, and level 6, aligning with chronological PATCH level score order. For activities 3, 5, and 7, the level 4 group rated the activity higher on average than the level 5 group, although they were both still lower than the level 6 group’s rating. The difference between the level 4 and 5 ratings were not significantly different.

Highlighted in blue in Table 4 to indicate significant p values, activities 2, 4, 5, 6, and 8 show a similar distribution of ratings across groups by PATCH level to the combined activity data, where levels 4 and 5 do not rate significantly differently, but level 6 rates the online healthcare science activities significantly higher than levels 4 and 5.

Activities 1, 3, and 7 show different distributions compared to the combined activity data, and each have a unique distribution. For Activity 1 – Competency, highlighted in yellow to indicate significant p values, both level 5 and 6 rated it significantly higher than level 4, while levels 5 and 6 did not rate it significantly differently between themselves. For Activity 3 – Interview, highlighted in green to indicate significant p values, level 6 rated it significantly higher than level 5, but not significantly different from level 4. This is because level 4 rated the activity higher than level 5 did, although it was not significantly different. Lastly, for Activity 7 – Cancer, there is no significant difference in rating between any of the levels 4, 5, and 6. Activity 7 was rated notably high across all engagement factors in the online learning activities study (Ozmizrak, 2023).

A combination clustered column and line chart is shown in Figure 1. The clustered columns show average rating by PATCH level group for each of the online healthcare science activities, while the lines show average rating by PATCH level group for all the activities combined.

Figure 1.

Combination clustered column and line chart for online healthcare science learning activities by PATCH level.

Combination clustered column and line chart for online healthcare science learning activities by PATCH level.

Discussion

Previous research was conducted using a version of the PATCH scale with nursing undergraduates focused on administering the tool within a class and reporting results (Alaban et al., 2020; Atay et al., 2014; Ozmizrak et al., 2023; Vijayalakshmi et al., 2014). This study extended the application of the PATCH scale towards examining different views of affinity for technology in an online learning environment. This is believed to be the first attempt to make use of the PATCH scale results to build additional meaningful analyses in the context of nursing undergraduate education. This study can serve as an example for extending the use of the PATCH scale for research in education.

Students who scored at the highest level of the PATCH, level 6, on average rated online learning activities significantly higher than students who scored lower on the PATCH (levels 4 or 5). There was not a significant difference within the lower levels. Level 6 of the PATCH represents students who may have an “idealistic” view of technology in healthcare, which aligns with greater engagement (interest, helpfulness, and relevance) and higher support of online (i.e. technological) learning activities.

This study did not contain groups of students who might have scored at the lowest levels of the PATCH (level 1 – cyberphobic, level 2 – unease). Only one individual scored level 3 – limited awareness of technology, and they were excluded from the full analysis. This study was confined to one large group of nursing students; therefore, the results may not transfer to other groups.

It is important to note that the learning activities included interfaces with online databases and technologies. The subject matter of healthcare science activities ranged from nursing competencies to pharmacogenomics and advances in genetic/genomic research for behavioral science among others. These are healthcare topics, which in themselves entail advanced skills to put into practice. The goal is for students to be able to transfer knowledge acquired in the classroom to future nursing practice. This may be facilitated by the ease by which students can apply technology in patient care. Efforts should be made to ensure that students are familiar with the technologies they will encounter in their training and future careers.

For students that have an affinity for technology in healthcare, online activities appear to be an effective strategy to support their engagement in learning.

References

Alaban, A., Almakhaytah, S., Almayouf, L., Alduraib, A., Althuyni, A., & Bin Meshaileh, L. (2020). Assessment of undergraduate nursing students’ attitudes and perceptions towards the use of computer technology in healthcare Settings. Iris Journal of Nursing and Care, 3(3), e1-3. doi:10.33552/IJNC.2020.03.000561

Atay, S., Arikan, D., Yilmaz, F., Aslanturk, N., & Uzun, A. (2014). Nursing and midwifery students’ attitudes to computer use in healthcare. Nursing Practice Today, Quarterly, 1(3), 147-154. Retrieved from https://npt.tums.ac.ir/index.php/npt/article/view/22

Barnard, A. (2016). Radical nursing and the emergence of technique as healthcare technology. Nursing Philosophy, 17(1), 8-18, doi: 10.111/nup.12103

Barchielli, C., Marullo, C., Bonciani, M., Vainieri, M. (2021). Nurses and the acceptance of innovations in technology-intensive contexts: The need for tailored management strategies. BMC Health Services Research, 21, 639. doi: 10.1186/s12913-021-06628-5

Dykes, S., & Chu, C.H. (2021). Now more than ever, nurses need to be involved in technology design: Lessons from the COVID-19 pandemic. Journal of Clinical Nursing,30(7-8), e25-e28. doi:  10.1111/jocn.15581

Kaminski, J. (2011). P.A.T.C.H. Assessment Scale v. 3. Retrieved from https://nursing-informatics.com/niassess/plan.html

Mansour, S., & Nogues, S. (2022). Advantages of and barriers to crafting new technologies in healthcare organizations: A qualitative study in the COVID-19 context. International Journal of Environmental Research and Public Health, 19(16), 9951. doi: 10.3390/ijerph19169951

Ozmizrak, P. (2023). An interdisciplinary approach to online genetics education [Doctoral dissertation, Clemson University]. https://tigerprints.clemson.edu/all_dissertations/3447

Ozmizrak, P., Boccuto, L., Brock Lowe, T. & DeLuca, J. (2023). Attitudes towards technology in healthcare among hybrid online nursing students. Canadian Journal of Nursing Informatics, 18(2).  https://cjni.net/journal/?p=11568

Strudwick, G. (2015). Predicting nurses’ use of healthcare technology using the Technology Acceptance Model: An integrative review. Computers, Informatics, Nursing, 33(5). 189-196. doi:10.1097/CIN0000000000000142

Vijayalakshmi, P., Ramachandra, S., & Math, S. (2014). Nursing students’ attitudes towards computers in health care: A comparative analysis. Journal of Health Informatics, 6(2), 46-52. Retrieved from https://jhi.sbis.org.br/index.php/jhi-sbis/article/view/286

Author Note

Pinar Ozmizrak, MS, PhD https://orcid.org/0000-0002-2689-5346

Luigi Boccuto, MD https://orcid.org/0000-0003-2017-4270

Tracy Brock Lowe, RN, MS, PhD https://orcid.org/0000-0003-1115-1405

Jane DeLuca, RN, MS, PhD, CPNP-PC https://orcid.org/0000-0003-0600-9500

We have no conflict of interest to disclose.

Correspondence concerning this article should be addressed to Pinar Ozmizrak, drpozmizrak@gmail.com

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