{
  "abstract": "Background: Artificial intelligence (AI) is becoming increasingly relevant for public health practice, yet scalable training models and validated usability instruments for AI chatbots remain limited in non-English contexts. This study aimed to assess AI knowledge, attitudes, and practices (KAP) among a sample of Italian public health professionals and to translate, culturally adapt, and psychometrically validate the Italian version of the Chatbot Usability Questionnaire (CUQ-IT).\n\nMethods: We conducted a three-phase study during the 57th Italian National Congress of Hygiene (October 2024): (i) a cross-sectional KAP survey; (ii) a 90-minute theoretical-practical workshop featuring guided prompt engineering and the collaborative development of a Custom GPT for vaccination counseling (VaxSense); and (iii) post-workshop usability evaluation with CUQ-IT. Internal consistency was estimated with Cronbach’s α.\n\nResults: Among 86 eligible respondents, participants reported moderate AI knowledge (mean 2.74±1.05/5), positive attitudes (3.87±0.73/5), but limited routine use (2.88±1.11/5). A statistically significant gender difference in self-rated AI knowledge was identified (p = 0.02). The CUQ-IT demonstrated high internal consistency (Cronbach’s α = 0.881) and robust performance across three domains: Personality, Response Handling, and Navigation Ease. The workshop-developed chatbot, VaxSense, achieved a good overall usability score (mean 66.60±15.29/100).\n\nConclusions: The findings highlight an “attitude-practice gap” where professionals are conceptually ready for AI but lack operational know-how. The CUQ-IT provides a standardized tool for chatbot evaluation in Italian settings. Furthermore, observed disparities in AI readiness underscore the need for inclusive, hands-on training models that bridge theoretical foundations with practical application to ensure the safe and equitable implementation of generative AI in public health.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "abstract",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "F Baglivo"
    },
    {
      "name": "A Gorga"
    },
    {
      "name": "G Vella"
    },
    {
      "name": "C Barbati"
    },
    {
      "name": "M Pecchioli"
    },
    {
      "name": "AL D’Atri"
    },
    {
      "name": "R Siliquini"
    },
    {
      "name": "W Mazzucco"
    },
    {
      "name": "A Odone"
    },
    {
      "name": "C Rizzo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/508xy6mk5parx.bib",
  "cited_by": 0,
  "doi": "10.1093/eurpub/ckag169.503",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13697643/",
  "id": "508xy6mk5parx",
  "issns": [
    "1101-1262",
    "1464-360X"
  ],
  "landing_url": "https://doi.org/10.1093/eurpub/ckag169.503",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "3. PARALLEL PROGRAMME"
    ],
    [
      "heading",
      "AcademicSubjects/MED00860"
    ],
    [
      "heading",
      "AcademicSubjects/SOC01210"
    ],
    [
      "heading",
      "AcademicSubjects/SOC02610"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7223695384",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13697643/pdf/?tool=EBI",
  "pmcid": "PMC13697643",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/508xy6mk5parx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:40Z",
      "native_id": "PMC13697643",
      "source": "pmc",
      "url": "https://doi.org/10.1093/eurpub/ckag169.503"
    },
    {
      "first_seen": "2026-10-30T12:21:34Z",
      "native_id": "PMC:PMC13697643",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13697643"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Training the public health workforce for generative AI: results from a practical workshop in Italy",
  "title_text": "Training the public health workforce for generative AI: results from a practical workshop in Italy",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:34:39Z",
  "url": "https://research.draconitic.com/paper/508xy6mk5parx",
  "venue": "The European Journal of Public Health",
  "versions": []
}