Evidence map›Paper›PMID 37932723›Full record

ArticleBMC public health2023

Risk perceptions of COVID-19, vocational identity, and employment aspirations of Chinese aviation students: a structural equation modeling approach.

Hongyao Qin, Yong Tang

Abstract read
In one paragraph

Article in BMC public health, 2023. 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.

Hongyao QinSchool of Broadcasting and Hosting, Sichuan Film and Television University, Chengdu, Sichuan, P.R. China.
Yong TangCollege of Tourism and Urban-rural Planning, Chengdu University of Technology, Chengdu, Sichuan, P.R. China. tangyong@cdut.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has wreaked havoc on the aviation and education sectors in China. This study examined the relationships between risk perceptions of the pandemic, vocational identity, and employment aspirations of Chinese aviation students.

methodsThe study used a convenience sampling approach to collect data (n = 276 respondents) from August 2 to 8, 2022. An online survey was sent via WeChat and QQ to Chinese students majoring in aviation service management who were under lockdown at six Chinese schools.

resultsIn spite of the strong support for the stringent COVID policies and full awareness of infection risk and protective measures, respondents were worried about the current unstable situation and felt fear for its severity and long-lasting symptoms. The casual path from career commitment to employment aspiration was supported, but high risk perceptions of the pandemic failed to have any psychological effect on the two constructs of vocational identity and employment aspirations.

conclusionsThe findings not only demonstrate the power of career commitment on employment aspirations but also reveal that a relatively high self-assessment of career proficiency may not necessarily lead to a clear career aspiration, possibly due to poor risk communication and insufficient career planning guidance. Thus, Chinese aviation students should improve their career proficiency and commitment, broaden their career options and adaptability, and have a clear career plan, in order to be well prepared for the fierce job market that will face the next wave of the ongoing pandemic.

Indexed as

AviationCOVID-19Communicable Disease ControlEast Asian PeopleEmploymentHumansLatent Class AnalysisPandemicsStudentsAviation studentsChinaCOVID-19Employment aspirationRisk perceptionStructural equation modelingVocational identity

Identifiers

PMID37932723
PMCPMC10629010

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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.