Evidence map›Paper›PMID 41102324›Full record

ArticleScientific reports2025

Exploring the relationship between after-hours work-related social media use and teacher job burnout.

Jiajun Jian, Min Zhou

Abstract read
In one paragraph

Article in Scientific reports, 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

2 authors.

Jiajun JianSouthwestern University of Finance and Economics, Chengdu, 610074, China.
Min ZhouSchool of Physical Education in Sichuan University, Chengdu, 610065, China. m18682753322@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Teaching is widely recognized as a profession characterized by high levels of occupational stress. This study investigates the impact of teachers' after-hours work-related social media communication (WRSMU) on job burnout, as well as the mediating role of work stress within the framework of the job demands-resources (JD-R) model. Furthermore, the study examines whether teachers' work values moderate the relationship between after-hours work-related social media use and work stress. The study targeted university teachers in Sichuan province, China, and employed a questionnaire survey to collect data. A total of 936 questionnaires were distributed, of which 842 were deemed valid. Statistical analyses were conducted using SPSS 23.0 and AMOS 24.0 to test the conceptual model. The results indicated that: (1) There is a positive relationship between work-related social media use (WRSMU) outside regular work hours and work stress. (2) Work stress plays a mediating role between WRSMU after work and job burnout. The antecedents of teacher job burnout are a topic worthy of attention in both academic and practice. This study highlights the complex relationship between teachers' after-hours work-related social media use (WRSMU), work stress, work values, and job burnout. The findings demonstrate that while Work-related public social media use after work hours can enhance communication efficiency, excessive use after work hours increases work stress and contributes to job burnout. In addition, future studies can investigate other moderating or mediating variables that affect teacher job burnout.

Indexed as

Burnout, ProfessionalFacultyOccupational StressSchool TeachersSocial MediaAdultChinaFemaleHumansMaleMiddle AgedSurveys and QuestionnairesAfter-hours work-related social media use (WRSMU)Job burnoutWork stressWork values

Identifiers

PMID41102324
PMCPMC12533047

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.