CoChat
Sign Up
Cited by 146Artificial Intelligence in Healthcare and EducationArtificial Intelligence in EducationHealthcare Education and Workforce Issues

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Factors influencing student nurses' readiness to adopt artificial intelligence (AI) in their studies and their perceived barriers to accessing AI technology: A cross-sectional study

Labrague, Leodoro J., Aguilar-Rosales, Rheajane, Yboa, Begonia C., Sabio, Jeanette B.·2023·Nurse Education Today
DOI 10.1016/j.nedt.2023.105945
Open this paper in CoChat — free account

Abstract

Background With the projected significant increase in the use of AI in nursing education, it becomes vital for nurse faculty to adequately equip student nurses with the necessary competences to effectively utilize AI in their studies. Ensuring that student nurses are prepared and ready to embrace AI technology is imperative for their successful integration into the healthcare workforce.Objective This study aimed to examine student nurses' readiness to embrace AI technology, explore associated factors, and identify perceived barriers to accessing AI technology.Design Cross sectional study.Settings One public-owned nursing school in the Philippines.Participants Three hundred twenty-three student nurses.Methods Data were collected using structured questionnaires. Descriptive statistics and multivariable analysis were performed to analyze the data.Results The results revealed that student nurses demonstrated moderate readiness to embrace AI in their studies (M = 2.906, SD = 0.692) and perceived moderate barriers to accessing AI technology (M = 2.336, SD = 0.719). Factors associated with students' readiness to embrace AI included self-rated technological proficiency (β = 0.170, p = 0.014), understanding of AI-powered technologies (β = 0.260, p < 0.001), and perceived AI use in nursing practice (β = 0.153, p = 0.022). The study also identified potential barriers to accessing AI technology, such as lack of computer skills to navigate AI, lack of AI knowledge and awareness, and time constraints.Conclusion The findings of this study provided valuable insights into the factors influencing student nurses' attitudes towards AI and shed light on their perceived barriers to accessing AI technology. By enhancing technological proficiency, increasing AI understanding, and providing practical experiences, nurse faculty can better prepare future nurses to effectively navigate the AI-driven healthcare environment and contribute to improved patient care outcomes.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.nedt.2023.105945

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 31 references

Research works printed but not linked: 2

  1. Buchanan, 2021, Predicted influences of artificial intelligence on the domains of nursing: Scoping review, JMIR Nurs., 4
  2. Damerji, 2021, Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting, Acc. Educ., 30, 107, 10.1080/09639284.2021.1872035 · DOI

Cited by

Sorted by citations

Items 101–146 of 146

Related

See all

Metadata

Authors
Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., & Sabio, J. B.
Affiliations
  • Loyola University Chicago (US)
  • Samar State University (PH)
Corresponding author
Labrague, Leodoro J.
ORCID
Labrague, Leodoro J.
Venue
Nurse Education Today
Publisher
Elsevier BV
Published
2023-11
Volume
130
Pages
105945
ISSN
0260-6917 (ISSN-L)
DOI
10.1016/j.nedt.2023.105945
OpenAlex
W4386002719
Cited by
145
References
31
Citations by year
2026: 55 · 2025: 60 · 2024: 27 · 2023: 2
FWCI
5.83
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Nurse Education Today (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Education › Artificial Intelligence in Education
  • Health Sciences › Nursing › Leadership and Management › Healthcare Education and Workforce Issues
Keywords
artificial intelligence, nursing students, technology readiness, perceived barriers, nursing education, technological competence, computer skills, time constraints
MeSH terms
Artificial Intelligence, Cross-Sectional Studies, Faculty, Humans, Nurses, Students, Nursing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review