Evidence map›Paper›PMID 41053687›Full record

ArticleBMC nursing2025

Current status of career adaptability among Chinese cardiovascular specialist nurses: a latent profile analysis.

Yanling Du, Chao Wu, Ziyue Gai, Li Gao, Shizhe He, Shujie Guo, Linyuan Zhang, Xiaolan Guo, Yonghong Yang

Abstract read
In one paragraph

Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Yanling Du *Department of Nursing, Air Force Medical University, Xi'an, China.
Chao Wu *Department of Nursing, Air Force Medical University, Xi'an, China.
Ziyue Gai *Department of Nursing, Air Force Medical University, Xi'an, China.
Li GaoDepartment of Nursing, Air Force Medical University, Xi'an, China.
Shizhe HeDepartment of Nursing, Air Force Medical University, Xi'an, China.
Shujie GuoDepartment of Nursing, Air Force Medical University, Xi'an, China.
Linyuan ZhangDepartment of Nursing, Air Force Medical University, Xi'an, China. 853909339@qq.com.
Xiaolan GuoDepartment of Cardiovascular Medicine, The Second Afliated Hospital of Air force Medical University, Xi'an, China. 610814723@qq.com.
Yonghong YangDepartment of Nursing, Shaanxi Provincial Hospital of Chinese Medicine, Xi'an, China. 648567185@qq.com.

Funding

Shaanxi Province Key Research and Development Program Project 2024SF-YBXM-666the Air Force Medical University Rapid Response Research Project 2023KXKT018Xijing Innovation Research Institute Joint Fund Project LHJJ24HL01
6 · The paper itself

Abstract

aimThis study aimed to explore the career adaptability status of cardiovascular specialist nurses (CSNs) through latent profile analysis (LPA), identify distinct subgroups and their demographic features, and determine factors influencing different adaptability categories.

backgroundCSNs play a vital role in treating and rehabilitating patients with cardiovascular conditions. However, the existing literature offers limited insights into the career adaptability of CSNs in China.

methodsA multicenter, cross-sectional survey involving 659 Chinese CSNs was conducted. LPA was utilized to classify career adaptability profiles based on responses to the Career Adaptation Abilities Scale Short Form (CAAS-SF). Influencing factors were assessed using the Conditions of Work Effectiveness Questionnaire-II (CWEQ-II) and the General Self-Efficacy Scale (GSES). Differences among identified profiles were analyzed through ANOVA, chi-square tests, and multinomial logistic regression to explore relevant socio-demographic characteristics and influencing variables.

resultsA four-profile model provided the best fit, identifying groups labeled as “high adaptability” (Class 4, n = 68, 10.5%), “good adaptability” (Class 3, n = 285, 42.6%), “moderate adaptability” (Class 1, n = 269, 41.2%), and “low adaptability” (Class 2, n = 37, 5.7%). Regression analysis indicated that highest educational level, monthly frequency of night shifts, opportunity, resources, informal empowerment, and self-efficacy were influencing factors associated with profile membership. CSNs working 1~2 night shifts per month (OR = 2.226, 95% CI: 1.014, 4.887) or possessing a bachelor’s degree (OR = 2.090, 95% CI: 1.440, 3.035) exhibited a greater likelihood of belonging to Class 3. Those reporting more opportunities (OR = 1.113, 95% CI: 1.001, 1.238; OR = 1.236, 95% CI: 1.028,1.485) tended toward membership in Classes 3 and 4. Furthermore, CSNs with greater resources (OR = 0.777, 95% CI: 0.620, 0.975; OR = 1.360, 95% CI: 1.116, 1.656) were significantly associated with membership in Classes 1 and 4. Additionally, higher informal empowerment (OR = 1.095, 95% CI: 1.001, 1.198) was associated with Class 3 membership. Enhanced self-efficacy (OR = 1.112, 95% CI: 1.069, 1.159; OR = 1.358, 95% CI: 1.268,1.455) increased the likelihood of classification into Classes 3 and 4.

conclusionsThese findings provide evidence to assist nursing administrators in developing training programs to enhance CSNs’ career adaptability. The variables identified as associated with profile membership may enable more tailored training strategies.

Indexed as

A latent profile analysisCardiovascular specialist nursesCareer adaptability

Identifiers

PMID41053687
PMCPMC12502321

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.