Evidence map›Paper›PMID 40529465›Full record

ArticleInternational journal of nursing sciences2025

Perceptions and experiences of generative artificial intelligence training to support research for Chinese nurses: A qualitative focus group study.

Ling Tong, Yirou Niu, Lixue Zhou, Shuai Jin, Yanling Wang, Qian Xiao

Abstract read
In one paragraph

Article in International journal of nursing sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Review
  9. Article
  10. Large language model-driven agents in nursing practice: A scoping review.International journal of nursing sciences · 2025
    Review
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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

6 authors.

Ling TongSchool of Nursing, Capital Medical University, Beijing, China.
Yirou NiuSchool of Nursing, Capital Medical University, Beijing, China.
Lixue ZhouSchool of Nursing, Capital Medical University, Beijing, China.
Shuai JinSchool of Nursing, Capital Medical University, Beijing, China.
Yanling WangSchool of Nursing, Capital Medical University, Beijing, China.
Qian XiaoSchool of Nursing, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Nurses' clinical research activities have contributed to optimizing the care process and improving patient outcomes, and generative artificial intelligence (GAI) may help clinical nurses strengthen their research skills. To support research, this study aimed to explore the Chinese nurses' perceptions and experiences of GAI training. Methods: This study used a descriptive qualitative design. The China Nurses Network conducted a three-day training session on "GAI for Nursing Research" theme, we selected 23 nurses by a convenience sampling method among participating in the training. The researchers conducted three focus group interviews at the end of each day. All focus groups were interviewed face-to-face to facilitate interaction, data collection, and observation. The data were analyzed using conventional content analysis and coded manually. Results: The results showed that nurses' use of GAI to support scientific research was dynamic and characterized by evolving perceptions and practices. Four themes and 11 sub-themes emerged from the analysis: 1) utilization efficacy: cope with research ability, affected by many factors; 2) booster research: growth and challenges go hand in hand; 3) role reversal: from GAI-dominated to nurse-dominated; 4) beautiful dream: more features on research, more assistants on clinical care. Conclusions: The effectiveness of GAI in supporting clinical nurses in conducting research is mainly limited by differences in personal research literacy, lack of ethical regulation, and information accuracy. In the future, it is necessary to improve nurses' relevant skills through specialized training and promote the standardization of technical regulations to ensure the appropriate application of GAI in nursing research.

Indexed as

ExperienceGenerative artificial intelligenceNursePerceptionsQualitative studyResearch capacity

Identifiers

PMID40529465
PMCPMC12168451

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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.