Background: Collaboration between Speech and Language Therapists (SLTs) and parents is considered best practice for children with developmental disorders. However, such collaborative approach is not yet implemented in therapy for children with developmental language disorders (DLD) in the Netherlands. Improving Dutch SLTs’ collaboration with parents requires insight in factors that influence the way SLTs work with parents. Aims: To explore the specific beliefs of Dutch SLTs that influence how they collaborate with parents of children with DLD. Methods and procedures: We conducted three online focus groups with 17 SLTs using a reflection tool and fictional examples of parents to prompt their thoughts, feelings and actions on specific scenarios. Data were organised using the Theoretical Domains Framework (TDF). Outcomes and results: We identified 34 specific beliefs across nine TDF domains on how SLTs collaborate with parents of children with DLD. The results indicate that SLTs hold beliefs on how to support SLTs in collaborating with parents but also conflicting specific beliefs regarding collaborative work with parents. The latter relate to SLTs’ perspectives on their professional role and identity, their approach towards parents, and their confidence and competence in working collaboratively with parents.
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Content moderation is commonly used by social media platforms to curb the spread of hateful content. Yet, little is known about how users perceive this practice and which factors may influence their perceptions. Publicly denouncing content moderation—for example, portraying it as a limitation to free speech or as a form of political targeting—may play an important role in this context. Evaluations of moderation may also depend on interpersonal mechanisms triggered by perceived user characteristics. In this study, we disentangle these different factors by examining how the gender, perceived similarity, and social influence of a user publicly complaining about a content-removal decision influence evaluations of moderation. In an experiment (n = 1,586) conducted in the United States, the Netherlands, and Portugal, participants witnessed the moderation of a hateful post, followed by a publicly posted complaint about moderation by the affected user. Evaluations of the fairness, legitimacy, and bias of the moderation decision were measured, as well as perceived similarity and social influence as mediators. The results indicate that arguments about freedom of speech significantly lower the perceived fairness of content moderation. Factors such as social influence of the moderated user impacted outcomes differently depending on the moderated user’s gender. We discuss implications of these findings for content-moderation practices.
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The field of data science and artificial intelligence (AI) is growing at an unprecedented rate. Manual tasks that for thousands of years could only be performed by humans are increasingly being taken over by intelligent machines. But, more importantly, tasks that could never be performed manually by humans, such as analysing big data, can now be automated while generating valuable knowledge for humankind
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