{
  "abstract": "Objectives: The Integrated Management of Childhood Illness (IMCI) faces challenges in capacity building and quality control. This trial aims to assess an electronic IMCI (eIMCI) platform in improving the effectiveness and efficiency in disease classification and management by community health workers (CHWs). Design: Cluster randomized controlled trial. Setting: Rural western China. Participants: 24 CHWs and 72 ill children aged 2 months to 5 years (3 children per CHW). CHWs were randomly assigned to intervention or control groups. Interventions: The intervention CHWs received online training and performed disease management using the eIMCI platform featuring integrated training modules and decision-support tools. The control group received traditional face-to-face training and used paper-based IMCI protocols. Main outcome measures: Proportion of children correctly diagnosed or classified by CHWs, as determined by a pediatric specialist. Relative risk (RR) between groups was estimated using Poisson Generalized Linear Mixed Models incorporating a random intercept for CHW to account for clustering of children within individual CHWs and adjusting for key covariates at both the CHW and child levels. Results: The intervention group (13 CHWs, 39 children) had a higher rate of correct classification (64.1%) compared to the control group (11 CHWs, 33 children) (39.4%, P = .056). Multivariable regression analysis confirmed this (RR = 2.1, 95% CI: 1.5–3.1; P < .001). No significant difference was found in correct treatment rates (38.5% vs. 27.3%, P = .316). Online training reduced time and costs by approximately 80%, though with a slight decrease in post-training evaluation scores. Conclusions: The eIMCI platform shows potential in enhancing IMCI implementation and significantly reducing the training burden in resource-limited settings.",
  "abstract_shown": true,
  "affiliations": [
    "The George Institute for Global Health, China Office, Beijing, China",
    "Duke-NUS Medical School, National University of Singapore, Singapore, Singapore",
    "Child Health Big Data Research Center, Capital Institute of Paediatrics, Beijing, China",
    "School of Computer Science and Engineering, Beihang University, Beijing, China",
    "Xinjiang Programme, Save the Children, Xinjiang, China",
    "Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, United Kingdom",
    "The George Institute for Global Health, Imperial College London, London, United Kingdom"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
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        0
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      "name": "Puhong Zhang"
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    {
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        1
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      "name": "Yang Zhao"
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    {
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        2
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      "name": "Jie Shang"
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    {
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        3
      ],
      "name": "Duolikun Nadila"
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    {
      "affs": [
        4
      ],
      "name": "Tao Li"
    },
    {
      "affs": [
        5
      ],
      "name": "Yu Liu"
    },
    {
      "affs": [
        6
      ],
      "name": "Gulizat Abdurixit"
    },
    {
      "name": "Jane Elizabeth Hirst"
    }
  ],
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  "doi": "10.1371/journal.pone.0352583",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
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  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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  "openalex": "W7213291100",
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  "published": "2026-09-15",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/bj61nn5bbcapy.ris",
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    {
      "first_seen": "2026-10-30T12:17:49Z",
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  "subjects": [
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      "origin": "plos:pediatrics",
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  "title": "Assessing the accuracy and efficiency of an electronic platform for managing childhood illnesses in rural China: A cluster randomized controlled trial",
  "title_text": "Assessing the accuracy and efficiency of an electronic platform for managing childhood illnesses in rural China: A cluster randomized controlled trial",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/bj61nn5bbcapy",
  "venue": "PLOS One",
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