Evidence map›Paper›PMID 36767707›Full record

ArticleInternational journal of environmental research and public health2023

Stress-Buffering Effects of Social Support on Tourism Employees during the COVID-19 Pandemic: A Moderated Mediation Model.

Huiyue Liu, Qiancai Tan, Huiping Mai

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

3 authors.

Huiyue LiuDepartment of Tourism Management, Shenzhen Tourism College, Jinan University, Shenzhen 518053, China.
Qiancai TanDepartment of Tourism Management, Shenzhen Tourism College, Jinan University, Shenzhen 518053, China.
Huiping MaiDepartment of Tourism Management, Shenzhen Tourism College, Jinan University, Shenzhen 518053, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the beginning of 2020, China's tourism industry has been severely impacted by the COVID-19 pandemic, and domestic tourism revenues have plummeted. Tourism employees have faced reduced working hours, job instability, shut down, and unemployment. In the context of the normalization of epidemic prevention, the tourism industry is recovering slowly and uncertainly, and many tourism employees face increasing employment stress. To investigate the relationship between social support and employment stress among tourism employees, 308 tourism employees were surveyed, and the mediating role of positive coping styles and the moderating role of psychological resilience were examined using structural equation modeling. The results revealed three key findings: social support significantly and negatively influenced the employment stress of tourism employees; positive coping styles significantly mediated the relationship between social support and employment stress among tourism employees; and psychological resilience moderated the relationship between social support and employment stress among tourism employees, as well as moderating the relationship between social support and positive coping style. The current findings help to deepen the understanding of the relationship between social support and employment stress, and they have important implications for alleviating the employment stress of tourism employees in the context of the pandemic.

Indexed as

COVID-19PandemicsAdaptation, PsychologicalHumansSocial SupportTourismemployment stresspositive coping stylespsychological resiliencesocial supporttourism employees

Identifiers

PMID36767707
PMCPMC9915072

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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.