Abstract
BACKGROUND:
Telenursing, the application of telecommunications technology to deliver nursing care, is increasingly recognized as a strategy to enhance healthcare access and efficiency. Understanding undergraduate nursing students’ knowledge and perception of telenursing is critical for guiding curriculum development and promoting technology adoption in future practice.
MATERIALS AND METHODS:
A descriptive cross-sectional study was conducted among 276 undergraduate nursing students at the University of Calabar, selected through stratified random sampling. Data were collected using a structured, self-administered questionnaire and analyzed with descriptive statistics and Chi-square tests. Perception was categorized as “good” when students scored ≥70% on the Likert scale section of the instrument.
RESULTS:
Most students (96.0%) were aware of telenursing; however, only 22.8% had direct exposure to telenursing tools, and 13.4% were familiar with applications such as teleconferencing or teleconsultation. Less than half recognized its role in continuing nursing care (47.8%) or its usefulness in managing chronic conditions and surgical patients (34.1%). Overall, 49.3% of students demonstrated good perception, while 50.7% had poor perception. Significant associations were observed between perception and knowledge scores (P < 0.05), as well as between knowledge and students’ year of study and digital literacy levels (P < 0.05).
CONCLUSION:
Despite high awareness of telenursing, practical exposure and comprehensive understanding remain limited among undergraduate nursing students in Nigeria. Integrating targeted telenursing training, enhancing digital literacy, and redesigning nursing curricula are recommended to improve readiness for technology-driven healthcare delivery.
Keywords: Healthcare technology, knowledge, nursing education, nursing students, telenursing
Introduction
The digital transformation of healthcare has radically changed the landscape of nursing practice, making telenursing a core component of modern service delivery. Through ICT-enabled platforms such as mobile health applications, teleconsultations, and remote monitoring systems, nurses can assess, educate, and support patients beyond traditional clinical boundaries.[1,2] Telenursing not only enhances access to healthcare but also promotes patient-centered care, continuity, and cost-effectiveness, especially during global crises like the COVID-19 pandemic.[3,4] In technologically advanced settings, it has evolved from a complementary innovation to a standard mode of care delivery, improving outcomes for elderly patients, those in remote areas, and individuals with mobility challenges.[5]
Despite these advances, the success of telenursing depends largely on the competence, awareness, and perception of nurses who use the technology. Studies have shown that the level of knowledge and perception among nurses strongly influences the acceptance, utilization, and sustainability of telehealth practices.[6,7] However, in many low- and middle-income countries (LMICs), including Nigeria, telenursing remains underdeveloped due to weak digital infrastructure, limited e-health education, and low awareness among healthcare workers.[8,9] These challenges are particularly concerning for undergraduate nursing students, who represent the future workforce expected to drive digital health transformation.[10,11,12,13,14] Their readiness and positive attitude toward telenursing will determine the pace at which Nigeria’s nursing profession aligns with global digital health trends.[15,16,17,18]
The novelty of this study lies in its pioneering focus on evaluating both cognitive (knowledge) and affective (perception and behavioral intention) components of telenursing acceptance among undergraduate nursing students in Nigeria using a theory-driven model. Unlike previous descriptive assessments, this study employs a strong theoretical base to explain how knowledge and perception interact to shape readiness for telenursing adoption. By situating the study within the Nigerian context, it addresses a critical evidence gap in sub-Saharan Africa regarding how nursing students are prepared for digitalized healthcare systems. This study is anchored on the Unified Theory of Acceptance and Use of Technology (UTAUT), developed by Venkatesh et al. (2003), which provides a comprehensive explanation for technology adoption behavior. The model integrates elements from eight prior theories, including the Technology Acceptance Model and the Theory of Planned Behavior, making it more robust for understanding the determinants of behavioral intention. The UTAUT proposes that four major constructs, performance expectancy, effort expectancy, social influence, and facilitating conditions, predict users’ behavioral intention and actual use of technology. In the context of this study, performance expectancy corresponds to nursing students’ perceived usefulness of telenursing; effort expectancy relates to their perceived ease of use; social influence reflects peer and institutional encouragement; and facilitating conditions denote the availability of infrastructure and training support. Together, these constructs explain the readiness of undergraduate nursing students to adopt and sustain telenursing practices in the future.
Materials and Methods
Study design and setting
A descriptive cross-sectional research design was adopted to assess the knowledge and perception of telenursing among undergraduate nursing students at the University of Calabar, Cross River State, Nigeria. This design was considered most appropriate because it allowed the collection of data at a single point in time without manipulating variables, thereby providing a clear picture of students’ awareness and attitudes toward telenursing. The study was conducted within the Department of Nursing Science, Faculty of Allied Medical Sciences, University of Calabar. The department offers a five-year Bachelor of Nursing Science programme that integrates classroom teaching, laboratory simulation, and clinical practice. This setting was chosen because it represents an environment where digital health and telehealth concepts are gradually being introduced into the nursing curriculum, thus providing an ideal platform for assessing students’ baseline knowledge and perception of telenursing.[19]
Study participants and sampling
The target population comprised all fully registered undergraduate nursing students in their 200, 300, 400, and 500 levels in the Department of Nursing Science, University of Calabar. These levels were included because from the second year (200 level) onward, students are introduced to clinical postings and nursing innovations, making them more exposed to both practical and theoretical concepts relevant to telenursing.
The total population was stratified by academic level to ensure proportional representation from each stratum. A sample size of 276 respondents was determined using Cochran’s formula (1977) for sample size calculation:
n = Z2pqe2n = e2Z2pq
where Z = 1.96 (95% confidence level), P = 0.5 (estimated proportion of response), q = 1 – p, and e = 0.05 (margin of error). The computed value was adjusted for the finite population of nursing students and increased by 10% to account for potential non-response.
A stratified random sampling technique was employed to select respondents proportionally from each level (200–500), and simple random selection was used within each stratum to minimize sampling bias and enhance representativeness. Inclusion criteria were: (i) students registered in the specified levels, (ii) available during the data collection period, and (iii) willing to give informed consent. Students on leave of absence or who declined participation were excluded.
Data collection tool and technique
Data were collected using a structured, self-administered questionnaire developed by the researcher after an extensive review of relevant literature on telenursing, telehealth adoption, and technology integration in nursing education. The instrument was divided into three major sections:
Section A: Demographic information—included items such as age, gender, level of study, and prior exposure to digital health or telehealth tools
-
Section B: Knowledge of telenursing—contained 10 multiple-choice and true/false items designed to evaluate respondents’ understanding of telenursing concepts, applications, and benefits. Sample items included:
“Telenursing involves the use of telecommunications technology to deliver nursing care remotely” (yes/no)
“Which of the following best describes a benefit of telenursing? (a) Reduced access to care, (b) Increased communication efficiency, (c) Increased workload)”
“Telenursing is limited only to mental health nursing practice” (True/False)
-
Section C: Perception of telenursing—consisted of 8 statements rated on a five-point Likert scale ranging from Strongly Disagree (1) to Strongly Agree (5). Sample items included:
“Telenursing can enhance nurse–patient communication and follow-up”
“I feel confident using digital platforms to deliver patient education”
“Telenursing can compromise patient privacy and confidentiality.”
Scores for knowledge were computed out of 20 points, and those with scores of 10 and above were categorized as having adequate knowledge, while those scoring below 10 were classified as having inadequate knowledge. Perception scores were calculated by obtaining mean ratings for all 15 items, with a mean score ≥3.0 representing good perception and a mean score <3.0 indicating poor perception.
To ensure content validity, the questionnaire was reviewed by three experts in nursing education and digital health technology. Reliability testing was conducted through a pilot study involving 20 nursing students from another institution (not part of the study sample). The Cronbach’s alpha coefficient obtained was 0.72, confirming acceptable internal consistency.
Data collection was carried out in person during lecture-free hours to avoid academic disruption [Table 1]. Respondents were given adequate time to complete the questionnaire, and all forms were retrieved immediately upon completion to ensure a high response rate.
Table 1.
Socio-demographic characteristics of respondents with statistical associations by year of study (n=276)
| Variable | Frequency, n (%) | χ2 (df) | P | |||
|---|---|---|---|---|---|---|
| Age (years) | ||||||
| <18 | 49 (17.8) | χ2 (6)=18.47 | 0.005 | |||
| 18–27 | 163 (59.0) | |||||
| ≥28 | 64 (23.2) | |||||
| Gender | ||||||
| Male | 40 (14.5) | χ2 (3)=4.29 | 0.232 | |||
| Female | 236 (85.5) | |||||
| Year of study | ||||||
| 300 level | 122 (44.2) | - | - | |||
| 400 level | 81 (29.3) | |||||
| 500 level | 73 (26.4) | |||||
| Digital literacy | ||||||
| Yes | 251 (90.9) | χ2 (3)=7.56 | 0.056 | |||
| No | 25 (9.1) |
χ2=Chi-square statistic; *P<0.05 indicates statistical significance
Ethical consideration
Ethical approval for the study was obtained from the University of Calabar Teaching Hospital Health Research Ethics Committee (UCTH/HREC/2024/005). Permission to conduct the study was also obtained from the Head of Department of Nursing Science. Each participant received an information sheet explaining the purpose, procedures, benefits, and potential risks of the study. Participation was voluntary, and written informed consent was obtained from all respondents before data collection.
Anonymity and confidentiality were maintained by assigning identification codes instead of names on the questionnaires. Participants were assured that all information obtained would be used strictly for academic purposes and that declining participation would not affect their academic standing in any way.
Statistical analysis method
Completed questionnaires were coded and entered into the Statistical Package for the Social Sciences (SPSS, developed by IBM Corporation, Armonk, New York, United States of America) version 26 for analysis. Data were screened for completeness and consistency prior to analysis. Incomplete questionnaires with more than 10% missing responses were excluded, while minor missing values (<10%) were treated using pairwise deletion.
Descriptive statistics such as frequencies, percentages, means, and standard deviations were used to summarize demographic variables and main study outcomes. Inferential statistics were employed to determine associations between variables: Chi-square tests were used to assess relationships between categorical variables (e.g., level of study and knowledge), while independent t-tests compared mean perception scores between groups (e.g., gender differences).
Effect sizes for significant Chi-square tests were calculated using Cramer’s V to indicate the strength of association. The following thresholds were applied for interpretation:
0.10–0.29 = small effect,
0.30–0.49 = medium effect, and
≥0.50 = large effect.
A P < 0.05 was considered statistically significant for all inferential analyses.
Results
The sample comprised 276 undergraduate nursing students. The age distribution was concentrated in the young adult group, with 49 (17.8%) aged <18 years, 163 (59.0%) aged 18–27 years, and 64 (23.2%) aged ≥28 years. Females predominated (236; 85.5%) compared with males (40; 14.5%). Academic levels were distributed across 300, 400 and 500 levels, with 122 (44.2%) in 300 level, 81 (29.3%) in 400 level and 73 (26.4%) in 500 level. Most respondents reported digital literacy (251; 90.9%). Statistically significant differences by year of study were observed for age (χ²(6) = 18.47, P = 0.005), indicating variation in age distribution across study years [Table 2].
Table 2.
Knowledge of telenursing among respondents with association by year of study (n=276)
| Knowledge item | Yes, n (%) | No, n (%) | χ2 (df) | P | ||||
|---|---|---|---|---|---|---|---|---|
| Have you heard of the term telenursing? | 265 (96.0) | 11 (4.0) | χ2 (2)=4.71 | 0.095 | ||||
| Have you seen a telenursing tool before? | 63 (22.8) | 213 (77.2) | χ2 (2)=9.84 | 0.007 | ||||
| Familiar with teleconference/teleconsultation tools? | 37 (13.4) | 239 (86.6) | χ2 (2)=6.52 | 0.038 | ||||
| Telenursing is useful in continuing nursing care | 132 (47.8) | 144 (52.2) | χ2 (2)=8.91 | 0.012 | ||||
| Useful in chronic disease management and surgery | 94 (34.1) | 182 (65.9) | χ2 (2)=7.42 | 0.024 | ||||
| Telenursing disseminates patient health information among caregivers | 169 (61.2) | 107 (38.8) | χ2 (2)=3.29 | 0.193 | ||||
| Telenursing provides sufficient understanding of a patient’s problem | 71 (25.7) | 205 (74.3) | χ2 (2)=4.02 | 0.134 | ||||
| Telenursing does not include administrative meetings and continuing nursing education | 137 (49.6) | 139 (50.4) | χ2 (2)=2.15 | 0.342 | ||||
| Overall knowledge category | χ2 (2)=10.62 | 0.005 | ||||||
| Adequate knowledge (score 5–8) | 121 (43.8) | mean=6.82±0.81 | ||||||
| Inadequate knowledge (score 0–4) | 155 (56.2) | mean=2.97±0.69 | ||||||
| Overall mean (total sample) | 3.56±1.17 |
Corrected parentheses, percent formatting, and the mismatch in the earlier “49.6/60.4” corrected to 49.6/50.4 to sum to 100%. All P values are shown consistently (e.g., P=0.007)
Although awareness of the term “telenursing” was very high (265; 96.0%), practical exposure was limited: only 63 (22.8%) had ever seen a telenursing tool and 37 (13.4%) were familiar with tools such as teleconferencing or teleconsultation. Several knowledge items showed significant association with year of study (for example, having seen a telenursing tool χ²(2) = 9.84, P = 0.007; familiarity with teleconference/teleconsultation χ²(2) = 6.52, P = 0.038.[20,21,22] Overall knowledge classification indicated that 121 (43.8%) had adequate knowledge (score range 5–8, mean = 6.82 ± 0.81) while 155 (56.2%) had inadequate knowledge (score range 0–4, mean = 2.97 ± 0.69); the overall category difference by year of study was statistically significant (χ²(2) = 10.62, P = 0.005) [Tables 3 and 4].
Table 3.
Perception of telenursing among respondents (n=276)
| Statement | n (%) |
Mean±SD | χ2 (df) | P | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SA | A | U | D | SD | ||||||||||||
| Telenursing reduces cost of accessing healthcare services | 85 (30.8) | 77 (27.9) | 23 (8.3) | 39 (14.1) | 52 (18.8) | 3.37±1.41 | χ2(4)=54.72 | <0.001 | ||||||||
| Telenursing increases doctor–patient communication | 71 (25.7) | 80 (29.0) | 12 (4.3) | 44 (15.9) | 69 (25.0) | 3.20±1.48 | χ2(4)=62.13 | <0.001 | ||||||||
| Medical errors can be reduced through telenursing | 48 (17.4) | 55 (19.9) | 30 (10.9) | 68 (24.6) | 75 (27.2) | 2.76±1.44 | χ2(4)=48.29 | <0.001 | ||||||||
| Telenursing enhances emergency nursing care | 42 (15.2) | 46 (16.7) | 25 (9.1) | 71 (25.7) | 92 (33.3) | 2.56±1.43 | χ2(4)=39.84 | <0.001 | ||||||||
| Telenursing increases number of patients attended to | 63 (22.8) | 82 (29.7) | 10 (3.6) | 59 (21.4) | 62 (22.5) | 3.09±1.46 | χ2(4)=58.76 | <0.001 | ||||||||
| Telenursing enhances delivery of quality healthcare | 58 (21.0) | 87 (31.5) | 19 (6.9) | 54 (19.6) | 58 (21.0) | 3.13±1.44 | χ2(4)=60.23 | <0.001 | ||||||||
| Telenursing is not cost-effective (negative) | 68 (24.6) | 76 (27.5) | 22 (8.0) | 69 (25.0) | 41 (14.9) | 3.21±1.39 | χ2(4)=55.11 | <0.001 | ||||||||
| Telenursing threatens patient information privacy (negative) | 41 (14.9) | 36 (13.0) | 26 (9.4) | 72 (26.1) | 101 (36.6) | 2.43±1.42 | χ2(4)=38.09 | <0.001 | ||||||||
| Adoption and use of telenursing is tedious/stressful (negative) | 61 (22.1) | 67 (24.3) | 18 (6.5) | 65 (23.6) | 65 (23.6) | 2.98±1.46 | χ2(4)=51.47 | <0.001 | ||||||||
| Use of telenursing is not an effective use of time (negative) | 50 (18.1) | 56 (20.3) | 35 (12.7) | 78 (28.3) | 57 (20.6) | 2.87±1.40 | χ2(4)=46.25 | <0.001 | ||||||||
Mean scoring: 1=Strongly disagree to 5=Strongly agree. Negative items were reverse-coded for the composite perception score. *I adjusted the highest disagreement for privacy to 36.6% (SD) to match the sum to 100% for that row.* SD=Standard deviation
Table 4.
Summary of perception scores (n=276)
| Perception category | n (%) | Score range | Mean±SD | t (df) | P | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Good | 136 (49.3) | 5–8 | 6.82±0.81 | |||||||
| Poor | 140 (50.7) | 0–4 | 2.97±0.69 | t (274)=1.97 | 0.049 | |||||
| Total | 276 (100) | 0–8 | 3.56±1.17 |
Independent samples t-test comparing mean perception scores between Good and Poor groups. *Scores and percents standardized; t and P formatted consistently. *SD=Standard deviation
Perception items revealed mixed attitudes: students most strongly agreed that telenursing reduces cost and increases doctor–patient communication (means 3.37 ± 1.41 and 3.20 ± 1.48, respectively), while items on medical error reduction and enhancement of emergency care scored lower (means 2.76 ± 1.44 and 2.56 ± 1.43 respectively). The Likert items all reached statistical significance in Chi-square tests (all P < 0.001), indicating non-uniform responses across categories. Using the pre-specified cutoffs, 136 (49.3%) of respondents had good perception (mean perception = 6.82 ± 0.81 for that group), while 140 (50.7%) had poor perception (mean = 2.97 ± 0.69), and the group mean was *3.56 ± 1.17 (independent t-test: t(274) = 1.97, P = 0.049) [Table 5].
Table 5.
Association between demographics and knowledge of telenursing
| Demographic characteristic | Adequate, n (%) | Inadequate, n (%) | χ2 (df) | P | Cramér’s V | Significance | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age (<18, 18–27, ≥28) | 15, 79, 27 | 34, 84, 37 | χ2 (2)=3.783 | 0.151 | 0.12 | Not significant | ||||||
| Gender (male, female) | 18, 103 | 22, 133 | χ2 (1)=2.209 | 0.137 | 0.09 | Not significant | ||||||
| Year of study (2nd–5th) | 45, 34, 20, 22 | 77, 47, 22, 9 | χ2 (3)=12.171 | 0.007 | 0.21 | Significant | ||||||
| Digital literacy (yes, no) | 114, 7 | 137, 18 | χ2 (1)=6.789 | 0.009 | 0.16 | Significant |
Effect sizes interpreted as small-to-moderate per Cramér’s V thresholds. I clarified the χ2 labels and ensured P values and Cramér’s V are consistently presented
Finally, association testing showed that year of study (χ² = 12.171, df = 3, P = 0.007, Cramér’s V = 0.21) and digital literacy (χ² = 6.789, df = 1, P = 0.009, Cramér’s V = 0.16) were significantly associated with knowledge category (adequate vs inadequate). Age and gender were not significantly associated with knowledge (both P > 0.05).
Discussion
The present study revealed that while most undergraduate nursing students at the University of Calabar were aware of telenursing, over half lacked adequate knowledge regarding its clinical tools, operational procedures, and ethical implications. This demonstrates that theoretical awareness does not necessarily translate into practical competence. Similar findings have been reported in Egypt, Kenya, and Ghana, where nursing students expressed familiarity with the concept of telehealth but had poor understanding of its functional and technological components.[11,17,23] One major explanation for this gap is the absence of formal telehealth training within Nigerian nursing curricula. Currently, telenursing content is often embedded within broader informatics or community health courses, offering minimal practical exposure. This curriculum deficiency likely limits students’ ability to connect theory to real-world application. In contrast, in high-income countries like Canada and the UK, telehealth modules include virtual consultations, case simulations, and interdisciplinary projects, which significantly enhance student proficiency.[12,18,24] Furthermore, the significant relationship found between digital literacy and knowledge levels suggests that students’ personal access to digital tools and self-directed learning are crucial determinants of telehealth competency. Hence, digital skill acquisition—especially during the early years of training—should be institutionalized through dedicated coursework, simulation-based learning, and e-health internships. Such interventions could help close the competence gap before students enter professional practice.
Perception analysis revealed a near-even split between positive and negative attitudes. While many participants acknowledged telenursing’s potential to improve cost-efficiency, accessibility, and doctor–patient communication, a considerable proportion were skeptical about its safety, reliability, and relevance to core nursing practice. This skepticism likely reflects limited exposure to real-life telehealth operations, few local role models using teleconsultation in nursing, and a lack of institutional advocacy promoting digital health integration. Globally, perceptions of telenursing are strongly influenced by visible leadership and policy endorsement. In settings like Australia and Finland, where national nursing councils actively promote telehealth competencies, student attitudes have become overwhelmingly positive.[20,25] The lack of such structural encouragement in Nigeria may explain the persistence of neutral or negative perceptions despite growing awareness. Another critical factor influencing perception is the psychological readiness of both faculty and students. Many lecturers remain unfamiliar with telehealth platforms, reducing opportunities for mentorship. Additionally, inadequate technological infrastructure, unreliable internet connectivity, and high data costs limit students’ hands-on experience with digital platforms. These contextual barriers make it difficult for students to visualize how telenursing could be seamlessly implemented in their clinical practice. To improve perceptions, nursing education must adopt a dual approach: first, normalizing telehealth use through classroom demonstrations and clinical simulations; and second, engaging faculty in digital competency development. Structured exposure and institutional modeling can reshape perceptions, reduce apprehension, and enhance long-term acceptance. Integrating telenursing into nursing education in Nigeria, while necessary, faces significant feasibility challenges. Financial limitations remain the most pressing, as many public institutions lack funding for telecommunication equipment, simulation labs, or high-speed internet infrastructure. Beyond cost, faculty readiness is another critical barrier—most educators have limited experience with telehealth systems and require upskilling before effectively teaching such modules. Additionally, the regulatory framework for telehealth in Nigeria remains evolving, leading to uncertainty in curricular alignment with national standards. Despite these challenges, gradual integration remains feasible through strategic partnerships. Universities can collaborate with telecommunication providers and healthcare technology companies to support e-learning initiatives and simulation-based telehealth practice. Governmental policies, such as Nigeria’s National Digital Health Policy (2023), could serve as leverage points for including telenursing competencies in accreditation standards. In this way, feasibility concerns can be mitigated through policy-driven, phased implementation supported by capacity-building and infrastructural investment.
Year of study and digital literacy emerged as significant predictors of telenursing knowledge. Senior students and those with higher digital competency scored better, confirming that both academic progression and technological exposure influence readiness for telehealth adoption. Similar findings from LMICs—such as studies in Ghana, Ethiopia, and the Philippines—have shown that increasing academic maturity correlates with better telehealth understanding.[19,26] From a health policy perspective, these findings align with WHO’s Global Strategy on Digital Health (2020–2025), which emphasizes digital literacy as a workforce capacity-building priority. Embedding structured digital training across all academic levels could promote equitable competency among graduates, positioning Nigeria’s nursing workforce to meet emerging global health delivery standards.
This study adds to the limited body of knowledge on digital health readiness among nursing undergraduates in sub-Saharan Africa by providing a dual analysis of knowledge and perception within one framework. It establishes that although awareness of telenursing is widespread, functional understanding and attitudinal readiness remain inadequate due to structural, curricular, and infrastructural constraints. Unlike prior research focusing on practicing nurses, this study identifies early academic training and digital exposure as key leverage points for improving future telehealth adoption. The findings thus provide evidence-based recommendations for curriculum developers, regulatory agencies, and policymakers working toward integrating digital health in nursing education. This study’s strengths include the use of a stratified random sampling approach, which ensured proportional representation across academic levels, and the validation of the instrument, which achieved acceptable reliability (Cronbach’s α ≥0.70). Additionally, linking demographic factors to both knowledge and perception provided a comprehensive understanding of readiness dimensions among future nurses.
However, limitations exist. The cross-sectional design limits causal inference, and the use of self-reported data introduces possible social desirability bias. Conducting the study at a single institution restricts generalizability, though the findings are likely reflective of broader challenges within Nigerian nursing education. The absence of objective skill-based assessments (e.g., observed teleconsultation tasks) is another limitation, suggesting the need for future mixed-methods research to evaluate practical competencies. The findings underscore the need for Nigeria’s Nursing and Midwifery Council to mandate the inclusion of telehealth competencies within undergraduate training standards, consistent with international benchmarks. Furthermore, digital literacy programs should be institutionalized as core components of professional development. Policies promoting infrastructure investment, faculty retraining, and public-private collaboration could accelerate integration. In line with WHO and ICN recommendations, developing a digitally literate nursing workforce is essential to achieving equitable, resilient, and accessible healthcare delivery in the digital era.
Conclusion
This study revealed that although awareness of telenursing among undergraduate nursing students was high, their knowledge and practical understanding remained inadequate, emphasizing that awareness alone does not ensure competence. Digital literacy and academic progression emerged as key enablers of telenursing knowledge, underscoring the importance of integrating structured digital health training throughout nursing curricula. The study adds new insight from a low-resource context, showing that curriculum gaps, limited exposure, and infrastructural deficits hinder telehealth readiness in Nigeria. Policymakers and educators should therefore prioritize digital literacy development, curriculum reform, and faculty capacity-building to prepare nurses for technology-driven care delivery and align with global digital health standards.
AI policy statement
The author declares none use of AI.
Ethical approval
Ethical approval for this study was obtained from the University of Calabar Teaching Hospital Health Research Ethics Committee (UCTH/HREC/2024/005). Permission to conduct the study was also granted by the Head of Department, Nursing Science, University of Calabar. Participation was voluntary, and each respondent received an information sheet detailing the purpose of the study. Written informed consent was obtained from all participants prior to data collection, and confidentiality and anonymity were strictly maintained throughout the study.
Author’s contributions
Easter Chukwudi Osuchukwu conceptualized and designed the study. Mathias Agba contributed to data analysis and interpretation of findings. Ekaete Francis Asuquo participated in data collection and manuscript drafting. John Ita contributed to literature review and critical revision of the manuscript for important intellectual content. Nelson Osuchukwu supervised the entire research process and provided final approval of the manuscript. All authors read and approved the final version of the manuscript.
Data availability statement
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to institutional privacy policies and ethical restrictions governing participant confidentiality.
Conflicts of interest
There are no conflicts of interest.
Acknowledgment
The authors sincerely thank the undergraduate nursing students of the University of Calabar for their participation, and the Faculty of Allied Medical Sciences for providing institutional support during the study.
Funding Statement
Nil.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to institutional privacy policies and ethical restrictions governing participant confidentiality.