Evidence map›Paper›PMID 39948539›Full record

ArticleBMC medical education2025

Core capabilities of Chinese centers for disease control and prevention public health personnel: a network analysis from Northeast China.

Yuxuan Wang, Ruiqian Zhuge, Kexin Wang, Nan Meng, Weiqi Huang, Yingxin Wang, Honghao Zhang, Xin Zhang, Qunkai Wang, Shanshan Gao and 3 more

Abstract read
In one paragraph

Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

13 authors.

Yuxuan Wang *Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China.
Ruiqian Zhuge *Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China.
Kexin WangDepartment of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China.
Nan MengDepartment of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China.
Weiqi HuangDepartment of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China.
Yingxin WangDepartment of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China.
Honghao ZhangDepartment of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China.
Xin ZhangDepartment of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China.
Qunkai WangDepartment of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China.
Shanshan GaoDepartment of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China.
Yunxia MaDepartment of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China.
Huan LiuDepartment of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China. liuhuan00813@163.com.
Qunhong WuDepartment of Social Medicine, Health Management College, Harbin Medical University, Harbin, 150081, China. wuqunhong@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has highlighted the continued inadequacy of the capability of Center for Disease Control and Prevention (CDC) public health personnel to respond effectively to emerging infectious disease outbreaks, as well as the urgent need for specific tools to assess the capability needed by CDC public health personnel in the post-pandemic era. Using network analysis, we aimed to analyze the comprehensive capacities of CDC public health staff and the relationships between these capacities. We also examined the impact of standardized public health training on their capacities and provided actionable recommendations for improving their training and overall capability.

methodsThis study employs a cross-sectional design. A self-developed questionnaire was used to evaluate the capabilities of public health personnel in CDC. Network analysis was conducted using the qgraph package in R (version 4.3.1) to construct a capability network model, while the bootnet package ensured the stability and reliability of the network through bootstrapping. The NetworkComparisonTest package was applied to compare network structures and identify differences between groups.

resultsOver half (51.80%, N = 11,912) of public health personnel rated their comprehensive capabilities as poor. Core capabilities, including research, motivation, and emergency response, were identified as pivotal within the capability network. The network stability coefficient for strength was 0.75, indicating reliable results. The capability networks of those who participated in standardized training differed significantly from those who did not (P = 0.04).

conclusionCDC public health personnel exhibit significant capability gaps, particularly in research and leadership. Standardized training provides some benefits but remains insufficient. Policymakers should address these gaps by aligning training content with critical capability needs, offering flexible and targeted training methods (e.g., virtual courses, self-paced modules), and implementing capability-based assessments to evaluate training outcomes.

Indexed as

Health PersonnelPublic HealthAdultChinaCross-Sectional StudiesFemaleHumansMaleSurveys and QuestionnairesCenter for disease control and preventionComprehensive capabilitiesNetwork analysisPublic health personnelStandardized training

Identifiers

PMID39948539
PMCPMC11827347

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.