Yunnan Jiama (paper horse prints), a representative form of intangible cultural heritage in southwest China, is characterized by subtle inter-class differences, complex woodblock textures, and heterogeneous preservation conditions, which collectively pose significant challenges for digital preservation and automatic image classification. To address these challenges and improve the computational analysis of Jiama images, this study proposes an enhanced object detection framework based on YOLOv8 integrated with a Global Attention Mechanism (GAM), referred to as YOLOv8-GAM. In the proposed framework, the GAM module is embedded into the high-level semantic feature extraction and multi-scale feature fusion stages of YOLOv8, thereby strengthening global channel–spatial interactions and improving the representation of discriminative cultural visual features. In addition, image augmentation strategies, including brightness adjustment, salt-and-pepper noise, and Gaussian noise, are employed to simulate real-world image acquisition and degradation conditions, which enhances the robustness of the model. Experiments conducted on a manually annotated Yunnan Jiama image dataset demonstrate that the proposed model achieves a mean average precision (mAP) of 96.5% at an IoU threshold of 0.5 and 82.13% under the mAP@0.5:0.95 metric, with an F1-score of 94.0%, outperforming the baseline YOLOv8 model. These results indicate that incorporating global attention mechanisms into object detection networks can effectively enhance fine-grained classification performance for traditional folk print images, thereby providing a practical and scalable technical solution for the digital preservation and computational analysis of intangible cultural heritage.

Background: Patient-centred decision-making is shaped by clinical information as well as patients’ emotional and relational experiences during care. This cross-sectional study examined how positive and negative emotions, physician trust, perceived empathy, and patient involvement were associated with patient-reported decision outcomes. Methods: A structured 30-item questionnaire using 5-point Likert scales was administered to 600 adult patients recruited by convenience sampling from Wuxi Hospital of Traditional Chinese Medicine, Jiangsu Province, China. The measurement model assessed negative emotions, positive emotions, trust in physicians, physician empathy, patient involvement, and decision outcomes. Reliability, exploratory factor analysis, confirmatory factor analysis, common-method-bias diagnostics, and covariance-based structural equation modelling were conducted. Results: The model showed acceptable-to-good fit (CFI = 0.961, TLI = 0.954, RMSEA = 0.043, SRMR = 0.038). Positive emotions (β = 0.312), physician trust (β = 0.398), and physician empathy (β = 0.361) were positively associated with patient involvement, whereas negative emotions were negatively associated with involvement (β = −0.284; all p < 0.001). Patient involvement partly mediated associations between emotional/relational factors and decision outcomes and also moderated predictor-outcome associations. Conclusions: In this single-institution sample, emotional states and relational perceptions were meaningfully associated with patient involvement and decision outcomes. Given the cross-sectional design, these findings indicate associations rather than causal effects; longitudinal and multi-centre studies are needed before causal or broadly generalizable conclusions can be drawn.

Yuxian paper cutting is a distinctive regional form within UNESCO-recognized Chinese paper cutting. Its opera figure images remain difficult to organize at scale because interpretation and cataloguing rely heavily on manual expertise, while automated tools for preliminary annotation and retrieval remain limited. This study formulates a domain-specific object detection task and constructs a curated dataset of 926 original images containing 976 annotated instances across three operational visual categories: armor-clad, robe-wearing, and female-role figures. A YOLOv8n-based detector is adapted by integrating the Spatial and Channel Synergistic Attention (SCSA) module and the Inner-IoU regression strategy. On the test set, the resulting configuration achieves 95.8% precision, 92.9% recall, 98.8% mAP@0.5, and 89.6% mAP@0.5:0.95, exceeding the YOLOv8n baseline by 3.0, 3.6, 2.9, and 2.9 percentage points, respectively. It also achieves a forward-pass throughput of 92.3 FPS under the reported GPU configuration. Among the evaluated configurations, the combined SCSA placement with Inner-IoU achieved the highest overall detection performance under the current dataset and experimental protocol. The results support its use for preliminary visual-category annotation, retrieval, and organization of Yuxian paper-cut opera figure resources. However, the findings are based on a small domain-specific dataset and require further validation across alternative data partitions and broader heritage collections.