Evidence map›Paper›PMID 42544282›Full record

ReviewNigerian medical journal : journal of the Nigeria Medical Association

Nigerian Nurses' and Nurse Educators' Readiness for Technology and Artificial Intelligence in Training and Practice: A Cross-Sectional Survey in Oyo State, Nigeria.

Blessing Osagumwendia Josiah, Muhammad Baqir Shittu, Grace Ireoluwa Adedokun, Joy Chioma Obialor, Gloria Oluwakorede Alao, Emily Kofoworola Adehanloye, Nifemi Tunrayo Babalola, Christy Boluwatife Adejumo, Victor Toyese Olarewaju, Gbohunmi Samuel Idowu and 4 more

Abstract readReview
In one paragraph

Review in Nigerian medical journal : journal of the Nigeria Medical Association. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Blessing Osagumwendia JosiahInstitute of Nursing Research, Nigeria.
Muhammad Baqir ShittuInstitute of Nursing Research, Nigeria.
Grace Ireoluwa AdedokunInstitute of Nursing Research, Nigeria.
Joy Chioma ObialorDepartment of Nursing Science, University of Jos, Plateau State, Nigeria.
Gloria Oluwakorede AlaoInstitute of Nursing Research, Nigeria.
Emily Kofoworola AdehanloyeInstitute of Nursing Research, Nigeria.
Nifemi Tunrayo BabalolaInstitute of Nursing Research, Nigeria.
Christy Boluwatife AdejumoInstitute of Nursing Research, Nigeria.
Victor Toyese OlarewajuInstitute of Nursing Research, Nigeria.
Gbohunmi Samuel IdowuInstitute of Nursing Research, Nigeria.
Abosede Peace OyegbadeInstitute of Nursing Research, Nigeria.
Emmanuel Chukwunwike EnebeliFaculty of Natural and Applied Science, Turks and Caicos Islands Community College, Grand Turk, Turks and Caicos Islands.
Emmanuel Olumide AdesuyiInstitute of Nursing Research, Nigeria.
Oluwadamilare AkingbadeInstitute of Nursing Research, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Globally, AI and technology are being integrated into nursing education and practice, providing students with realistic patient care scenarios for safe, hands-on learning. These technological advancements and AI have also aided Nurses in documentation and data input, allowing nurses to focus more on patient care. However, the adoption of AI and other technologies in Nigeria's nursing environment is still in its early stages compared to more developed countries.This study investigates the readiness of Nigerian nurses and nurse educators in Oyo state to integrate technology and Artificial Intelligence (AI) into their training and clinical practice. Methodology: A quantitative survey design was employed, with 115 registered nurses from Oyo State, Nigeria, participating. Results: The study reveals moderate levels of technological use, with 73% of respondents having used some form of technology or AI tools in their practice. Perceptions towards AI integration were predominantly positive, with over 95% agreeing on its potential to improve healthcare delivery and patient outcomes. Readiness to utilise AI was high, with almost all respondents (97.4%) ready to utilise AI tools in practice. Key barriers to adoption included unavailability of necessary technologies (65.2%), insufficient infrastructure (62.7%), and inadequate training (60.8%). The study found significant associations between previous utilisation of AI, practice designation and readiness to utilise AI, as well as between nursing roles and perceptions of AI integration. Conclusion: These findings highlight the need to develop and implement comprehensive AI training programs for Nurses, focusing on hands-on experience and understanding of AI applications in patient care. Given the high level of readiness, tailored training programs can help close the gap created by inadequate training.

Indexed as

AIArtificial Intelligencedigital LiteracyNursing TechnologiesReadiness and Utilisation.

Identifiers

PMID42544282
PMCPMC13429094

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LicenceCC BY-NC-SA
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.