Evidence map›Paper›PMID 38539169›Full record

ArticleBMC nursing2024

Nurses' perceptions, experience and knowledge regarding artificial intelligence: results from a cross-sectional online survey in Germany.

Domenic Sommer, Lukas Schmidbauer, Florian Wahl

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Article in BMC nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 2 pooled it
–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

43 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

3 authors.

Domenic Sommer *Deggendorf Institute of Technology, Dieter-Görlitz-Platz 1, Deggendorf, 94469, Bavaria, Germany. domenic.sommer@th-deg.de.
Lukas Schmidbauer *Deggendorf Institute of Technology, Dieter-Görlitz-Platz 1, Deggendorf, 94469, Bavaria, Germany.
Florian WahlDeggendorf Institute of Technology, Dieter-Görlitz-Platz 1, Deggendorf, 94469, Bavaria, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNursing faces increasing pressure due to changing demographics and a shortage of skilled workers. Artificial intelligence (AI) offers an opportunity to relieve nurses and reduce pressure. The perception of AI by nurses is crucial for successful implementation. Due to a limited research state, our study aims to investigate nurses' knowledge and perceptions of AI.

methodsIn June 2023, we conducted a cross-sectional online survey of nurses in Bavaria, Germany. A convenience sample via care facilities was used for the questionnaire oriented on existing AI surveys. Data analysis was performed descriptively, and we used a template analysis to evaluate free-text answers.

results114 (♀67.5 %, ♂32.5 %) nurses participated. Results show that knowledge about AI is limited, as only 25.2 % can be described as AI experts. German nurses strongly associate AI with (i) computers and hardware, (ii) programming-based software, (iii) a database tool, (iv) learning, and (v) making decisions. Two-thirds of nurses report AI as an opportunity. Concerns arise as AI is seen as uncontrollable or threat. Administration staff are seen as the biggest profiteers.

conclusionEven though there is a lack of clear understanding of AI technology among nurses, the majority recognizes the benefits that AI can bring in terms of relief or support. We suggest that nurses should be better prepared for AI in the future, e.g., through training and continuing education measures. Nurses are the working group that uses AI and are crucial for implementing nursing AI.

Indexed as

Artificial intelligenceGermanyHealthcareNurseNursing education

Identifiers

PMID38539169
PMCPMC10967047

What OpenQuestion holds

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LicenceCC BY
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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.