Evidence map›Paper›PMID 40762402›Full record

Observational studyJournal of advanced nursing2026

Bridging the Digital Divide: A Multi-Method Evaluation of Nursing Readiness for Digital Health Technology.

Gordana Dermody, Daniel Wadsworth, May El Haddad, Roslyn Prichard, Alex Benson, Tim Benson, Alison Craswell

Abstract readObservational Study
In one paragraph

Observational study in Journal of advanced nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

7 authors.

Gordana DermodyUniversity of the Sunshine Coast, Sippy Downs, Queensland, Australia.ORCID https://orcid.org/0000-0003-0489-3881
Daniel WadsworthUniversity of the Sunshine Coast, Sippy Downs, Queensland, Australia.ORCID https://orcid.org/0000-0003-1015-1120
May El HaddadUniversity of the Sunshine Coast, Sippy Downs, Queensland, Australia.ORCID https://orcid.org/0000-0003-2328-685X
Roslyn PrichardUniversity of the Sunshine Coast, Sippy Downs, Queensland, Australia.ORCID https://orcid.org/0000-0001-8057-6605
Alex BensonR-Outcomes Ltd, Newbury, UK.
Tim BensonR-Outcomes Ltd, Newbury, UK.
Alison CraswellUniversity of the Sunshine Coast, Sippy Downs, Queensland, Australia.ORCID https://orcid.org/0000-0001-8603-3134

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimThe aim of this study was to explore the digital health technology readiness of nurses, nursing students, nurse-academics, and nurses in leadership roles. Workforce digital readiness impacts the adoption of digital health technologies and quality and safety outcomes. This study sought to identify key factors affecting nurses' readiness for specific digital health technologies and provide recommendations to accelerate readiness levels in alignment with rapidly advancing digital health technologies.

designCross-sectional multi-method study.

methodsAn online survey was followed by semi-structured interviews. Survey data (N = 160) were analysed using descriptive and inferential statistics, whereas qualitative responses (N = 8 interviews, 43 open-ended responses) were thematically analysed.

resultsParticipants were confident regarding openness to innovation, reporting highest confidence Levels around telehealth, wearable devices, and information technology. The lowest confidence scores were seen in health smart homes technology, followed by health applications, social media, patient online resources, and EHRs. Four themes were developed from the qualitative interviews including 'opportunities for efficient ways of working', 'digital technology turning experts into novices', 'disillusionment between expectation and reality' and 'shared responsibility for development of digital expertise'. Open-ended data was focused on the need for comprehensive education, ongoing support, and infrastructure improvements to prepare healthcare professionals for digital health environments.

conclusionsNotable findings include age-related differences, the need for shared responsibility in workforce preparation, and a link between problem-solving ability and help-seeking. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Low confidence among nurses around the use of digital health technologies such as electronic health records, in-home monitoring technology, and other wearable technologies could impact adoption readiness. Because patient safety is increasingly and inextricably linked to digital health technologies, nurses must not only be digital health literate but also included in the design and implementation process of these technologies. REPORTING

methodThis study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for the reporting of cross-sectional survey research, and the Consolidated Criteria for Reporting Qualitative (COREQ) research guidelines. PATIENT OR PUBLIC CONTRIBUTION: Limited patient and public involvement was incorporated, focusing on feedback from digital health researchers and practitioner-academics during the academic peer review process. Their insights informed the clarity and relevance of the survey design and data interpretation, ensuring alignment with real-world workforce development priorities in nursing.

Indexed as

Attitude of Health PersonnelDigital DivideDigital TechnologyStudents, NursingAdultCross-Sectional StudiesDigital HealthFemaleHumansMaleMiddle AgedSurveys and QuestionnairesTelemedicinedigital health readinesshealth information technologynursing educationworkforce development

Identifiers

PMID40762402
PMCPMC12994680

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.