The Online Health Safety Gap: Consensus Alignment Does Not Imply Safety in Peer-to-Peer Health Narratives.

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.