Evidence map›Paper›PMID 41111997›Full record

ArticleJournal of nursing management2025

Developing a Competency Model for Mobile Nurses in Chinese Tertiary Hospitals: A Delphi-AHP Approach.

Na Fan, Yan-Ling Yin, Sai-Na Zhang, Rui Yan, Shi-Fan Han

Abstract read
In one paragraph

Article in Journal of nursing management, 2025. 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

5 authors.

Na FanSchool of Nursing, Shanxi Medical University, Taiyuan, Shanxi, China.ORCID 0009-0004-1750-7382
Yan-Ling YinNursing Department, Second Hospital of Jilin University, Changchun, China.
Sai-Na ZhangSchool of Nursing, Shanxi Medical University, Taiyuan, Shanxi, China.
Rui YanSchool of Nursing, Shanxi Medical University, Taiyuan, Shanxi, China.
Shi-Fan HanSchool of Nursing, Shanxi Medical University, Taiyuan, Shanxi, China.ORCID 0000-0003-4448-1689

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: To establish an evidence-based competency framework for mobile nurses in tertiary hospitals, addressing critical gaps in workforce optimization during healthcare crises. Background: Mobile nurses play pivotal roles in emergency response and cross-departmental coordination, yet standardized competency models remain lacking, hindering evidence-based workforce optimization. Methods: A mixed-methods study integrating: (1) Literature synthesis (85 articles, 2015-2025); (2) semistructured interviews with 32 clinicians from four Chinese tertiary hospitals and (3) two-round Delphi-AHP consultation ( Results: The final model comprises 3 domains-Personality Traits (44.47%), Knowledge/Skills (37.02%), and Professionalism (18.51%)-with 10 subdomains and 40 indicators. Delphi consensus strengthened significantly between rounds (mean CV reduction: 0.25 ⟶ 0.08, Conclusion: This first evidence-based competency framework addresses mobile nurses' unique demands in dynamic care environments, providing actionable metrics for recruitment, training, and performance evaluation. Implications for Nursing Management: The framework enables precise talent allocation, tailored training, and dynamic performance evaluation, aligning with World Health Organization (WHO)'s Health Workforce 2030 targets through its dual focus on technical proficiency and adaptive resilience.

Indexed as

Clinical CompetenceNursesAdultChinaDelphi TechniqueFemaleHumansMaleQualitative ResearchTertiary Care Centerscompetency frameworkmobile nursenursing competencyresource optimisationworkforce agility

Identifiers

PMID41111997
PMCPMC12534139

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.