Evidence map›Paper›PMID 41657698›Full record

ArticleFrontiers in public health2026

Training needs and influencing factors among rural-oriented general practitioners in Chongqing, China: a cross-sectional survey and latent profile analysis.

Xintao Huang, Xingxin Deng, Enze Liu, Chen Zhang, Min Tang, Xun Lei, Huisheng Deng

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Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xintao HuangDepartment of General Practice, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Xingxin DengDepartment of General Practice, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Enze LiuDepartment of General Practice, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Chen ZhangDepartment of General Practice, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Min TangDepartment of Academic Affairs, College of General Practice (Fifth Clinical College) of Chongqing Medical University, Chongqing, China.
Xun LeiCollege of Public Health, Chongqing Medical University, Chongqing, China.
Huisheng DengDepartment of General Practice, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Rural-oriented General Practitioners (GPs) are a key workforce to alleviate physician shortages in central and western China and improve the quality of primary healthcare services. However, they currently face high attrition rates. Targeted and effective continuing medical education (CME) can partially improve their retention rates. Nevertheless, existing CME programs do not align with these GPs' practical needs. This study aims to assess these GPs' CME needs and associated influencing factors, providing evidence-based support for the development of CME curricula and related policy formulation. Methods: A cross-sectional survey was conducted via stratified sampling among 508 rural-oriented GPs across 38 counties of Chongqing from July to August 2025. Data were collected using the Chinese-validated version of the Hennessy-Hicks Training Needs Analysis Questionnaire, which had been translated and validated before implementation, and a researcher-designed general information questionnaire. The questionnaires covered basic demographics, perceived importance of professional skills, and self-rated proficiency scores. Descriptive statistics and comparative analysis were used to identify the overall priority training needs. Latent profile analysis was employed to identify the types of training needs, and multinomial logistic regression analysis was conducted to identify factors influencing training needs. Results: Rural-oriented GPs reported significant training needs across all 37 skills ( Conclusion: Training demands show heterogeneity across subgroups among rural-oriented GPs in Chongqing and are affected by multiple factors. A significant training gap exists with the most urgent need in

Indexed as

Education, Medical, ContinuingGeneral PractitionersNeeds AssessmentRural Health ServicesAdultChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and Questionnairescontinuing medical educationcontinuous professional developmentgeneral practitionerslatent profile analysisprimary health caretraining needs analysis

Identifiers

PMID41657698
PMCPMC12872738

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LicenceCC BY
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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.