The diagnostic interview for psychoses (DIP): development, reliability and applications

Background. We describe the development, reliability and applications of the Diagnostic Interview for Psychoses (DIP), a comprehensive interview schedule for psychotic disorders. Method. The DIP is intended for use by interviewers with a clinical background and was designed to occupy the middle ground between fully structured, lay-administered schedules, and semi-structured., psychiatrist-administered interviews. It encompasses four main domains: (a) demographic data; (b) social functioning and disability; (c) a diagnostic module comprising symptoms, signs and past history ratings; and (d) patterns of service utilization Lind patient-perceived need for services. It generates diagnoses according to several sets of criteria using the OPCRIT computerized diagnostic algorithm and can be administered either on-screen or in a hard-copy format. Results. The DIP proved easy to use and was well accepted in the field. For the diagnostic module, inter-rater reliability was assessed on 20 cases rated by 24 clinicians: good reliability was demonstrated for both ICD-10 and DSM-III-R diagnoses. Seven cases were interviewed 2-11 weeks apart to determine test-retest reliability, with pairwise agreement of 0.8-1.0 for most items. Diagnostic validity was assessed in 10 cases, interviewed with the DIP and using the SCAN as 'gold standard': in nine cases clinical diagnoses were in agreement. Conclusions. The DIP is suitable for use in large-scale epidemiological studies of psychotic disorders. as well as in smaller Studies where time is at a premium. While the diagnostic module stands on its own, the full DIP schedule, covering demography, social functioning and service utilization makes it a versatile multi-purpose tool.

Modelling context information with ORM

Context-aware applications rely on implicit forms of input, such as sensor-derived data, in order to reduce the need for explicit input from users. They are especially relevant for mobile and pervasive computing environments, in which user attention is at a premium. To support the development of context-aware applications, techniques for modelling context information are required. These must address a unique combination of requirements, including the ability to model information supplied by both sensors and people, to represent imperfect information, and to capture context histories. As the field of context-aware computing is relatively new, mature solutions for context modelling do not exist, and researchers rely on information modelling solutions developed for other purposes. In our research, we have been using a variant of Object-Role Modeling (ORM) to model context. In this paper, we reflect on our experiences and outline some research challenges in this area.

Takeovers: Acceptance facilities revisited in the Patrick case

Takeovers undertaken in Australia are highly regulated transactions. Once shareholders in the target accept an offer they have a limited opportunity, if any at all, to reconsider or revoke their acceptance in the light of new circumstances. Arguably, this explains target shareholders reluctance to accept an offer made for their shares under a takeover. The problem of shareholder inertia in takeovers has been identified by bidders, who have sought to induce bid acceptance through the use of innovative mechanisms. The efficacy of the Acceptance Facility mechanism was recently revisited in the Panel decision in Patrick Corporation Ltd’s takeover by Toll Holdings Ltd.

The impact of target board recommendations in Australian takeovers

This paper examines the impact of targe board recommendations on the probability of the bid being successful in the Australian takeovers context. Specifically, we model the success rate of the bid as a binary dependent variable and target board recommendations or the board hostility as our key independent variable by using logistic regression framework. Our model also includes bid structures and conditions variables (such as initial bid premium, bid conditions, toehold, and interlocking relationship) and bid events (such as panel and bid duration) as our control variables. Overall, we find board hostility has statistically significant negative effect on the success rate of the bid and almost all control variables (except for the initial bid premium) are statistically significant with the correct sign. That is, we find toehold, the percentage of share required to make the bid becomes successful, and the unconditional bid have positive impact on the success rate of the bid, at least as predictive determinants prior to the release of any hostile recommendation. Consistent with Craswell (2004), we also find the negative relation between interlocking relationship and the success rate of the bid. Our finding supports that from target investors’ point of view, interlock is consistent with the negative story of self interest by directors. Finally, like Walking (1985), we find that the initial bid premium does not have influence on the success rate of the bid. Hence our results reinstate Walking’s bid premium puzzle in Australian context.