Lei Zhao

China’s recent anti-corruption efforts in the healthcare sector have focused on senior hospital administrators and on procurement processes for pharmaceuticals and medical supplies, with comparatively little attention paid to frontline clinical physicians. Yet the everyday practices of frontline physicians—including excessive testing, overtreatment, and overprescription—constitute a major source of cost inflation and have contributed to a substantial erosion of patient trust. Drawing on principal–agent theory and on responsive regulation theory, this study examines whether introducing a statutory fiduciary duty between physicians and patients, enforced through emerging medical audit mechanisms, is associated with reductions in physicians’ self-reported willingness to engage in inappropriate practices. We conducted a sequential exploratory mixed-methods study comprising (i) a qualitative phase of eight semi-structured interviews with practicing clinical physicians in Beijing and Shenyang (March–July 2023), analyzed using reflexive thematic analysis, and (ii) a between-subjects scenario-based questionnaire experiment with 120 practicing physicians recruited from hospitals in Jinan, Qingdao, Shenyang, and Beijing. Participants in the quantitative phase were systematically allocated to an experimental condition (a proposed statutory fiduciary duty enforced through medical audits and graduated sanctions) or a control condition (the existing ethics-based framework). The primary outcome was self-reported willingness to reduce inappropriate medical practices, measured on a five-point Likert scale. Statistical analyses comprised an independent-samples t test, a Mann–Whitney U test, multiple linear regression with HC3 heteroscedasticity-robust standard errors, and a three-step hierarchical moderation analysis with mean-centered predictors and simple-slope decomposition. Participants in the experimental condition reported a significantly higher willingness to reduce inappropriate medical practices than those in the control condition (M = 4.07, SD = 0.73 vs. M = 1.93, SD = 0.69; t(118) = 16.46, p < 0.001; mean difference = 2.13, 95% CI [1.88, 2.39]; Cohen’s d = 3.01). Group assignment remained a robust predictor in a multiple regression model adjusting for department, years of practice, perceived medical audit credibility, and perceived institutional frequency of inappropriate practices (B = 2.45, SE = 0.14, β = 0.96, p < 0.001; R² = 0.846). Hierarchical moderation analysis showed that perceived audit credibility significantly moderated the experimental effect (interaction B = 1.15, SE = 0.12, t = 9.44, p < 0.001, 95% CI [0.91, 1.39]; ΔR² = 0.064). Predicted simple-slope group differences increased monotonically from 0.89 points at audit level 2 to 4.33 points at audit level 5. Three converging qualitative themes—institutional tolerance of inappropriate practices, structural rationalization of misconduct, and sensitivity to enforceable sanctions—triangulated with the quantitative findings. The findings provide preliminary, exploratory evidence that a statutory fiduciary duty enforced through credible medical audit mechanisms is associated with substantially greater reported willingness to reduce inappropriate medical practices than ethics-based exhortation alone. The widening gap between conditions as audit credibility increased is consistent with the central prediction of responsive regulation theory: the credibility of escalation at the apex of the regulatory pyramid is what makes cooperative norms at its base operative. Limitations include a small non-probability sample, reliance on self-reported behavioral intentions, the contrast in stimulus specificity between conditions, and the absence of pre-registration. The findings should be regarded as hypothesis-generating, providing a basis for larger pre-registered studies rather than definitive evidence of causal effects.

Objective: Tangent Space Mapping using the geometric structure of the covariance matrices is an effective method to recognize multiclass motor imagery. Compared with the traditional CSP method, the Riemann geometric method based on TSM takes into account the nonlinear information contained in the covariance matrix, and can extract more abundant and effective features. Moreover, the method is an unsupervised operation, which can reduce the time of feature extraction. However, EEG features induced by MI mental activities of different subjects are not the same, so selection of subject-specific discriminative EEG frequency components play a vital role in the recognition of multiclass MI. In order to solve the problem, a discriminative and multi-scale filter bank tangent space mapping algorithm is proposed in this article to design the subject-specific Filter Bank so as to effectively recognize multiclass MI tasks. Methods: On the 4-class BCI competition IV-2a dataset, first, a non-parametric method of multivariate analysis of variance based on the sum of squared distances is used to select discriminative frequency bands for a subject; next, a multi-scale FB is generated according to the range of these frequency bands, and then decompose multi-channel EEG of the subject into multiple sub-bands combined with several time windows. Then TSM algorithm is used to estimate Riemannian tangent space features in each sub-band and finally a liner Support Vector Machines is used for classification. Main Results: The analysis results show that the proposed discriminative FB enhances the multi-scale TSM algorithm, improves the classification accuracy and reduces the execution time during training and testing. On the 4-class BCI competition IV-2a dataset, the average session to session classification accuracy of nine subjects reached 77.33 ± 12.3%. When the training time and the test time are similar, the average classification accuracy is 2.56% higher than the latest TSM method based on multi-scale filter bank analysis technology. When the classification accuracy is similar, the training speed is increased by more than three times, and the test speed is increased two times more. Compared with Supervised Fisher Geodesic Minimum Distance to the Mean, another new variant based on Riemann geometry classifier, the average accuracy is 3.36% higher, we also compared with the latest Deep Learning method, and the average accuracy of 10-fold cross validation improved by 2.58%. Conclusion: Research shows that the proposed DMFBTSM algorithm can improve the classification accuracy of MI tasks. Significance: Compared with the MFBTSM algorithm, the algorithm proposed in this article is expected to select frequency bands with good separability for specific subject to improve the classification accuracy of multiclass MI tasks and reduce the feature dimension to reduce training time and testing time.

Drawing on theories of social exchange and social information processing, we examined whether the influence of psychological contract breach on in-role performance and organization-directed citizenship behavior (OCBO) depends work group climate levels, specifically procedural justice climate and power distance climate. The findings supported our hypothesis that psychological contract breach exhibits a stronger influence on in-role performance and OCBO among members of units with favorable procedural justice climates. Support for a hypothesized moderating role of power distance climate was less conclusive. We discuss the implications of our model and findings for theories of employee-organization relationships and practice.

Common spatial pattern is an effective algorithm for extracting electroencephalogram features of motor imagery ; however, CSP mainly aims at multichannel EEG signals, and its effect in extracting EEG features with fewer channels is poor—even worse than before using CSP. To solve the above problem, a new combined feature extraction method has been proposed in this study. For EEG signals from fewer channels, wavelet packet transform, fast ensemble empirical mode decomposition, and local mean decomposition were used to decompose the band-pass filtered EEG into multiple time–frequency components, and the corresponding components were selected according to the frequency characteristics of MI or the correlation coefficient between its time–frequency components and the original EEG signal. Furthermore, phase space reconstruction was performed on the selected components after the three time-frequency decompositions, the maximum Lyapunov index was calculated, and the features were reconstructed; then, CSP projection mapping was used for the reconstructed features. The support vector machine probability output model was trained by the obtained three mappings. Probability outputs by three different support vector machines were then obtained. Finally, the classification of test samples was determined by the fusion of the Dempster–Shafer evidence theory at the decision level. The results showed that the accuracy of the proposed method was 95.71% on data set III of BCI competition II, which was 2.88% higher than the existing methods. On data set IIb of BCI competition IV, the average accuracy was 86.60%, which was 2.3% higher than the existing methods. This study verified the effectiveness of the proposed method and provided an approach for the research and development of the MI-BCI system based on fewer channels.

Research on the relationship between emotions and job performance is ubiquitous, yet few scholars have examined the combined effects of different emotions. Drawing on the broaden-and-build theory and conservation of resources theory, we propose that employees’ daily emotion fluctuations will affect their service performance in opposite directions. Furthermore, we propose these effects will be moderated by psychological [i.e., regulatory emotional self-efficacy ] and physiological characteristics of the employees. Based on the experience sampling method, data obtained from 187 frontline employees of 35 bank branches over 18 consecutive days supports our hypotheses.