Evidence map›Paper›PMID 40730986›Full record

ArticleBMC nursing2025

What matters most? Identifying key resource gain and loss factors affecting nurses' work engagement and job burnout in digital healthcare contexts.

Jianing Guo, Zhenduo Zhang, Huan Xiao, Xiaoqian Zu, Kun Zhou, Ruoyi Qu

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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

6 authors.

Jianing GuoSchool of Economics and Management, Dalian University of Technology, Dalian, Liaoning, 116024, China.
Zhenduo ZhangSchool of Economics and Management, Dalian University of Technology, Dalian, Liaoning, 116024, China. zhangzhenduo@dlut.edu.cn.
Huan XiaoPostdoctoral Mobile Station of Management Science and Engineering, Nanchang University, Nanchang, Jiangxi, 330031, China.
Xiaoqian ZuCollege of Architectural Science and Engineering, Yangzhou University, Yangzhou, Jiangsu, 225127, China.
Kun ZhouCollege of Economics and Management, Nanjing Agricultural University, Nanjing, Jiangsu, 210095, China. zhoukunxm@njau.edu.cn.
Ruoyi QuInstitute of Health Professions Education Assessment and Reform, China Medical University, Shenyang, Liaoning, 110122, China. ryqu@cmu.edu.cn.

Funding

2024 China State owned Enterprise Research Institute State owned Enterprise Theoretical Research Open Fund gqkfjj-zx-202403Humanities and Social Sciences Youth Project of the Ministry of Education 22YJC630211National Natural Science Foundation of China 72301054Social Science Planning Found Project of Liaoning Province 2024lslqurckt-026
6 · The paper itself

Abstract

backgroundWork engagement and burnout are two of the most significant work states in nurses. The increasing prevalence of information and communication technologies (ICT) in the workplace means that nurses are required not only to actively embrace technological advancements for their own well-being and to enhance the quality of nursing care, but also to simultaneously learn to cope with the mental and physical health issues arising from ICT use. Nevertheless, research on the work status of Chinese nurses in the context of ICT demands remains scant, particularly in large-sample, multi-center empirical studies.

aimto investigate the distinct predictors of work engagement and job burnout among Chinese nurses in the context of ICT demands, and to clarify the differential mechanisms underlying these two work states.

methodsA cross-sectional survey (N = 1612) was conducted among Chinese nurses from January to February 2024, utilizing questionnaires to collect demographic information, work-related details, job resources, job stressors, work engagement and job burnout within the context of ICT usage. Descriptive statistics and one-way ANOVA were employed to identify control variables based on demographic and work-related information. Pearson correlation analysis explored relationships between job resources, job stressors, and nurses' work status. Regression models for work engagement and job burnout were constructed, with LASSO regression selecting the most critical influencing variables.

resultsThe LASSO regression model of nurses' work engagement includes seven explanatory variables, of which the ones that pass the significance test are harmonious work passion, positive affect, and self-efficacy. The LASSO regression model of nurses' job burnout included 9 variables, of which the statistically significant ones were ego depletion, negative affect, job insecurity, and ICT demands.

conclusionsThis study makes three innovative contributions. First, it identified three key resource gain factors that significantly related to nurses' work engagement. Second, it pinpointed four principal resource loss factors that contribute to nurses' burnout. Third, it distinguished between the two concepts of work engagement and burnout and examined the differentiated factors that influence them, thereby providing a nuanced understanding of the distinct mechanisms at play. CLINICAL TRIAL NUMBER: Not Applicable.

Identifiers

PMID40730986
PMCPMC12306028

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.