Evidence map›Paper›PMID 42577444›Full record

ArticleFrontiers in public health2026

Bridging the epistemic gap: a psychological acceptance model for the cross-cultural health communication of traditional Chinese medicine.

Jing Li, Ji Chen, Ping Yi, Fuyuan Lei, Yan Lv, Hong Du

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Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jing LiSchool of Foreign Languages, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Ji ChenSchool of Foreign Languages, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Ping YiSchool of Foreign Languages, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Fuyuan LeiSchool of Foreign Languages, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Yan LvSchool of Foreign Languages, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Hong DuSchool of Foreign Languages, Chengdu University of Traditional Chinese Medicine, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional Chinese medicine (TCM) is now included in the ICD-11, reflecting its growing use within systems of medical pluralism worldwide. Its global communication, however, continues to encounter cognitive friction. This friction reflects an epistemic gap between TCM's holistic, pattern-based logic and the reductionist framework of biomedicine, a gap that linguistic translation cannot close on its own. The present study develops the TCM Psychological Acceptance Model (TCM-PAM) to address this challenge and to consider how the ontological insecurity often experienced by non-Chinese audiences might be alleviated. The study adopts a theory-driven, two-phase design. In the first phase, we draw on cultural schema theory, cognitive balance theory, and a systems-oriented Knowledge Translation (KT) paradigm to build a preliminary framework. The framework is then refined through consultations with a transdisciplinary panel of senior experts in TCM practice, health communication, and global health governance. The resulting TCM-PAM reconceptualizes acceptance as a three-stage cognitive trajectory: an initial stage of perception and affective filtering, a middle stage of semantic negotiation and cognitive mapping, and a final stage of internalization and identification. Three determinants are identified as central to this trajectory: perceived compatibility, cognitive cost, and trust transfer. Expert consultation suggests that pairing cognitive scaffolding such as metaphoric mapping with institutional recognition reduces ontological insecurity and supports the integration of TCM into the recipient's evolving health identity rather than its dismissal as something foreign. By foregrounding cognitive compatibility and dynamic trust transfer, the TCM-PAM offers a framework for working across the epistemic gap through co-constructive dialog rather than unidirectional information transfer. The model speaks to a broader case for medical pluralism within global health governance, one in which integrating different medical systems rests on a serious engagement with their distinct health ontologies. Such engagement, in our view, is part of what equitable and inclusive Universal Health Coverage requires.

Indexed as

Health CommunicationMedicine, Chinese TraditionalModels, PsychologicalCross-Cultural ComparisonHumanscognitive scaffoldingcross-cultural health communicationontological insecuritypsychological acceptancesystems-oriented knowledge translationtraditional Chinese medicineuniversal health coverage

Identifiers

PMID42577444
PMCPMC13454201

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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.