Evidence map›Paper›PMID 41334415›Full record

ArticleFrontiers in public health2025

The impact of professional identity on nurses' mental health status after the COVID-19 public health emergency: a cross-sectional survey.

Nan Xie, Liqun Zou, Zhenfei Yuan

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. 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.

  1. Article
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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.

Nan XieDepartment of Emergency Medicine, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.
Liqun ZouDepartment of Emergency Medicine, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.
Zhenfei YuanDepartment of Emergency Medicine, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: The purpose of this study was to explore the correlation between the mental health status and professional identity of Chinese nurses after the COVID-19 public health emergency. Method: An e-questionnaire survey was designed, and nurses from 4 Chinese hospitals were included in our study. The questionnaires were used to measure nurses' professional identity, mental health, and sleep quality. Logistic regression analyses were used to explore their relationships. Results: The multiple logistic regression analysis suggested that medical assistance, PSQI score, and level of professional identity were independent risk factors for anxiety, and the results of multiple logistic regression analysis suggested that a medium level of professional identity and the PSQI score were independent factors for depression. Discussion: The results show that nurses' professional identity is negatively related to anxiety and depression after experiencing the COVID-19 public health emergency. Implications for practice: After the COVID-19 public health emergency, managers urgently need to pay attention to nurses' mental health issues, which may help improve nurses' professional identity, increase their work enthusiasm, and benefit their career development.

Indexed as

COVID-19Mental HealthNursesNursing Staff, HospitalSocial IdentificationAdultAnxietyChinaCross-Sectional StudiesDepressionFemaleHumansMaleMiddle AgedPublic HealthSARS-CoV-2anxietyChinaCOVID-19cross-sectional studydepressionnursing staffprofessional identitysleep quality

Identifiers

PMID41334415
PMCPMC12665649

What OpenQuestion holds

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LicenceCC BY
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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.