Using Force: Australian Newspaper Depictions of Contacts Between the Police and Persons Experiencing Mental Illness

The media creates and sustains knowledge about socially relevant issues such as crime, mental illness, and policing. Past research suggests that while the media portrayal of the police is undoubtedly complex, their portrayal of people experiencing mental illness remains mainly negative. The depiction of their encounters in the mass media is of considerable interest because of the influential role the media has on public opinion and debate; however, there is a lack of current research in this area. This study aimed to examine media items in the major newspapers across Australia between January 2006 and December 2007 (n = 77) to investigate their portrayal of this interface. The dominant themes encountered in the initial analysis depicted people experiencing mental illness as dangerous and a threat to the public, where the mental health system has failed to manage them adequately. Further analyses found that people experiencing mental illness were stigmatised in a third of the media items reviewed and were represented significantly more negatively in these encounters than the police. Implications of these findings and some suggestions regarding possible strategies that could improve socially responsible media reporting and public discourse on mental illness are proposed.     

Development and validation of the Interpersonal Emotion Management Scale

Interpersonal Emotion Management (IEM) strategies represent behaviours targeted at managing negative emotions in others. This paper describes the development and validation of the four‐dimensional IEM strategies scale. Four studies were conducted to assess the psychometric properties of the scale, including content, discriminant, and criterion validity. Results provided strong support for the four‐dimensional measure of IEM strategies, distinct from conceptually related constructs, and predictive of subordinates’ trust in their supervisor.     

Fluency Profiling System: An automated system for analyzing the temporal properties of speech

The temporal characteristics of speech can be captured by examining the distributions of the durations of measurable speech components, namely speech segment durations and pause durations. However, several barriers prevent the easy analysis of pause durations: The first problem is that natural speech is noisy, and although recording contrived speech minimizes this problem, it also discards diagnostic information about cognitive processes inherent in the longer pauses associated with natural speech. The second issue concerns setting the distribution threshold, and consists of the problem of appropriately classifying pause segments as either short pauses reflecting articulation or long pauses reflecting cognitive processing, while minimizing the overall classification error rate. This article describes a fully automated system for determining the locations of speech–pause transitions and estimating the temporal parameters of both speech and pause distributions in natural speech. We use the properties of Gaussian mixture models at several stages of the analysis, in order to identify theoretical components of the data distributions, to classify speech components, to compute durations, and to calculate the relevant statistics.