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The study seeks to unpack the effect of distributed leadership on teacher professionalism, and the mediating roles of collaborative learning and relational trust in the Chinese cultural context.

The proposed framework was examined based on the questionnaire data from 522 primary and secondary school teachers in China using structural equation modeling.

It was found that distributed leadership had a direct positive impact on collaborative learning and relational trust, which also exerted the direct positive impact on teacher professionalism. However, distributed leadership cannot directly affect teacher professionalism in China. Only through the full mediation of collaborative learning and relational trust, could distributed leadership facilitate teacher professionalism in an indirect way. The proportion of sequence mediating effect was the highest, followed by the single mediating role played by relational trust.

We have demonstrated to international scholars the indirect value of distributed leadership in enhancing teacher professionalism in China. The results not only enrich the existing influencing mechanism framework of professionalism, but also provide valuable implications that school leadership does not have a completely positive effect on teacher professionalism. Only when the empowering leadership style is truly perceived by teachers and strengthens their collaborative learning and mutual trust, can a team of capable educators be formed to promote teacher professionalism. It also indicates that teacher professionalism becomes a systematic and structural process requiring support from multiple parties, such as schools, leaders, colleagues and self.

Depression has become one of the most serious and prevalent mental health problems worldwide. The rise and popularity of social networks such as microblogs provides a wealth of psychological data for early depression detection. Language use patterns reflect emotional states and psychological traits. Differences in language use between depressed and general users may help predict and diagnose early depression. Existing work focuses on depression detection using users' social textual emotion expressions, with less psychology-related knowledge.

In this paper, we propose an RNN-capsule-based depression detection method for microblog users that improves depression detection accuracy in social texts by combining textual emotional information with knowledge related to depression pathology. Specifically, we design a multi-classification RNN capsule that enhances emotion expression features in utterances and improves classification performance of depression-related emotional features. Based on user emotion annotations over time, we use integrated learning to detect depression in a user’s social text by combining the analysis results with components such as emotion change vector, emotion causality analysis, depression lexicon and the presence of surprising emotions.

In our experiments, we test the accuracy of RNN capsules for emotion classification tasks and then validate the effectiveness of different depression detection components. Finally, we achieved 83% depression detection accuracy on real datasets.

The paper overcomes the limitations of social text-based depression detection by incorporating more psychological background knowledge to enhance the early detection success rate of depression.