ArticleBMC medical education2020
Performance of China's new medical licensing examination for rural general practice.
Article in BMC medical education, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed.
- Association between adverse practice experiences and positional competence among residents: a multicenter cross-sectional study.BMC medical education · 2025Article
- The Metacognitive Enhancement Triad model enhances learning outcomes in first-year emergency medicine residents.Frontiers in medicine · 2025Article
- Article
- Current status and continuing medical education need for general practitioners in Tibet, China: a cross-sectional study.BMC medical education · 2024Article
- Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses.BMC medical education · 2024Article
- Article
- Job Performance of Medical Graduates With Compulsory Services in Underserved Rural Areas in China: A Cohort Study.International journal of health policy and management · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo evaluate the performance of China's new medical licensing examination (MLE) for rural general practice, which determines the number of qualified doctors who can provide primary care for China's rural residents, and to identify associated factors.
methodsData came from all 547 examinees of the 2017 MLE for rural general practice in Hainan province, China. Overall pass rates of the MLE and pass rates of the MLE Step 1 practical skills examination and Step 2 written exam were examined. Chi-square tests and multivariable logistic regression were used to identify examinee characteristics associated with passing Step 1 and Step 2, respectively.
resultsOf the 547 examinees, 68% passed Step 1, while only 23% of Step 1 passers passed Step 2, yielding an 15% (82 of 547) overall pass rate of the whole examination. Junior college medical graduates were 2.236 (95% CI, 1.127-4.435) times more likely to pass Step 1 than secondary school medical graduates. Other characteristics, including age, gender, forms of study and years of graduation, were also significantly associated with passing Step 1. In contrast, examinees' vocational school major and Step 1 score were the only two significant predictors of passing Step 2.
conclusionsOur study reveals a low pass rate of China's new MLE for rural general practice in Hainan province, indicating a relatively weak competency of graduates from China's alternative medical education. An effective long-term solution might be to improve examinees' clinical competency through mandating residency training for graduates of China's alternative medical education.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.