{
  "abstract": "Online health information systems evaluate content by its alignment with medical consensus, treating that alignment as a reliable signal of safety. In peer-to-peer health discourse, that assumption fails: advice that is factually accurate is not necessarily safe to act on. This paper identifies and empirically validates the Online Health Safety Gap, a structural misspecification in which medically accurate content can carry substantial health risk. Across 704 health narratives (699 classified), two scores computed from non-overlapping feature sets, Narrative Truth Distance for epistemic divergence and Narrative Risk Score for health risk potential, share only 4.9% of their variance (r = 0.222, p < 0.001). Consequently, 39.6% of narratives fall in the two off-diagonal quadrants that single-axis systems mishandle by construction: aligned-but- risky content (25.2%) invisible to fact-checking, and divergent-but-safe content (14.4%) incorrectly suppressed. The same independence constrains what existing benchmarks can measure. On an expert-labeled misinformation benchmark of 437 posts containing 127 misinformation instances, the highest discrimination attained is Youdens J of 0.349, by a supervised classifier trained on those labels directly, which neither frozen biomedical embeddings nor a prompted large language model improves upon. Labels encoding factual accuracy therefore carry little signal about behavioral risk, and benchmarks of this construction cannot evaluate risk-aware assessment. To operationalize the gap, we introduce the Classification Quadrant, which maps epistemic divergence and health risk potential onto four governance categories with distinct intervention implications, and argue for a shift from fact- centric evaluation to risk-aware assessment.",
  "abstract_shown": true,
  "affiliations": [
    "University of Maryland, Baltimore County (UMBC)"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "O. Clark"
    },
    {
      "name": "K. Joshi"
    },
    {
      "name": "Z. Ahmed"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/daa8r5amrk94t.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.18.26363448",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.18.26363448v1.full",
  "id": "daa8r5amrk94t",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.18.26363448v1",
  "language": null,
  "licence": "CC-BY-ND",
  "licence_label": "CC BY-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "3312|Misinformation and Its Impacts"
    ]
  ],
  "openalex": "W7213932299",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.18.26363448v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/daa8r5amrk94t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:05Z",
      "native_id": "10.64898/2026.09.18.26363448",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.18.26363448v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "The Online Health Safety Gap: Consensus Alignment Does Not Imply Safety in Peer-to-Peer Health Narratives.",
  "title_text": "The Online Health Safety Gap: Consensus Alignment Does Not Imply Safety in Peer-to-Peer Health Narratives.",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:05Z",
  "url": "https://research.draconitic.com/paper/daa8r5amrk94t",
  "venue": "medRxiv",
  "versions": []
}