Women’s knowledge, attitude, and practice regarding cervical precancerous lesions: a cross-sectional study in Beijing, China

This study revealed inadequate knowledge, moderate attitude, and practice toward cervical precancerous lesions among women in Beijing. Educational interventions should be developed to enhance knowledge in this regard.

Authors

Institutions

Publication Details

Journal
Frontiers in Public Health
Published
2024-10-02
DOI
https://doi.org/10.3389/fpubh.2024.1433718
Citations
4
Primary Topic
Cervical Cancer and HPV Research
Type
article
Field-Weighted Citation Impact
1.55
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Women’s knowledge, attitude, and practice regarding cervical precancerous lesions: a cross-sectional study in Beijing, China

Qianping Wang, Huan Liu, Lingyan Wang, Xia Zhou
4 citations
Frontiers in Public Health
Cervical Cancer and HPV Research
1.55
article

Women’s knowledge, attitude, and practice regarding cervical precancerous lesions: a cross-sectional study in Beijing, China

Qianping Wang, Huan Liu, Lingyan Wang, Xia Zhou
article en
4 citations

Abstract

This study revealed inadequate knowledge, moderate attitude, and practice toward cervical precancerous lesions among women in Beijing. Educational interventions should be developed to enhance knowledge in this regard.

Frontiers in Public HealthVol. 12
China-Japan Friendship Hospital (CN), Beijing Shijingshan Hospital (CN), Beijing Hospital (CN), Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine (CN)
Openalex Percentile: Top 16%
Cervical Cancer and HPV Research
1.55
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.