Evidence map›Paper›PMID 35480590›Full record

ArticleFrontiers in public health2022

Predicting Rural Women's Breast Cancer Screening Intention in China: A PLS-SEM Approach Based on the Theory of Planned Behavior.

Yanjun Sun, Jiawei Yuan, Wuqianhui Liu, Banghui Qin, Zhiqing Hu, Jianwei Li, Yuan He

Abstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

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3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Yanjun SunInstitute of Medical Humanities, Nanjing Medical University, Nanjing, China.
Jiawei YuanSchool of Health Policy and Management, Nanjing Medical University, Nanjing, China.
Wuqianhui LiuThe First School of Clinical Medicine, Nanjing Medical University, Nanjing, China.
Banghui QinInstitute of Medical Humanities, Nanjing Medical University, Nanjing, China.
Zhiqing HuInstitute of Medical Humanities, Nanjing Medical University, Nanjing, China.
Jianwei LiDepartment of Material and Child Health, Lianyungang Material and Child Health Hospital, Lianyungang, China.
Yuan HeInstitute of Medical Humanities, Nanjing Medical University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: It was reported that the incidence of breast cancer (BC) was the highest among cancers worldwide. The breast cancer screening (BCS) program is regarded as an effective preventive measure. However, rural women's willingness to participate in the BCS program is relatively low. To provide measures to prevent BC, it is necessary for the government to identify the influencing factors of rural women's BCS intention. Methods: A cross-sectional study was conducted among 3,011 rural women by a convenience sampling method through face-to-face interviews on a self-designed questionnaire based on the theory of planned behavior (TPB). The partial least square structural equation model (PLS-SEM) was conducted to determine the predictors of BCS intention, and a multi-group analysis (MGA) of age was performed to identify if there were differences in all hypotheses between different age groups. Results: There were still rural women who have not been screened for BC in five years (41.7%). The research model of rural women's intention to accept this prevention against BC was rational. All of the hypotheses are supported. Especially, subjective norm (SN) (β = 0.345, Conclusion: The TPB with the addition of PB, knowledge, ML&SA, and A&E can provide the theoretical basis for the policy intervention that aims to enhance the rural women's BCS willingness. MGA of age is conducive to promoting the implementation of the BCS policy. The findings are of great significance to improve rural women's health levels.

Indexed as

Breast NeoplasmsIntentionChinaCross-Sectional StudiesEarly Detection of CancerFemaleHumansLeast-Squares Analysisbreast cancer screening intention (BCS intention)multi-group analysis (MGA)rural womenthe partial least square structural equation model (PLS-SEM)the theory of planned behavior (TPB)

Identifiers

PMID35480590
PMCPMC9035887

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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.