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Objective: to investigate associations between major life events and prognosis independent of treatment type: (1) after adjusting for clinical prognostic factors and socio-demographics; (2) amongst patients with depressive episodes at least six-months long; and (3) patients with a first life-time depressive episode.Methods: six RCTs of adults seeking treatment for depression in primary care met eligibility criteria, individual patient data (IPD) were collated from all six (n = 2858). Participants were randomized to any treatment and completed the same baseline assessment of life events, demographics and clinical prognostic factors. Two-stage random effects meta-analyses were conducted.Results: reporting any major life events was associated with poorer prognosis regardless of treatment type. Controlling for baseline clinical factors, socio-demographics and social support resulted in minimal residual evidence of associations between life events and treatment prognosis. However, removing factors that might mediate the relationships between life events and outcomes reporting: arguments/disputes, problem debt, violent crime, losing one's job, and three or more life events were associated with considerably worse prognoses (percentage difference in 3-4 months depressive symptoms compared to no reported life events =30.3%(95%CI: 18.4-43.3)).Conclusions: assessing for clinical prognostic factors, social support, and socio-demographics is likely to be more informative for prognosis than assessing self-reported recent major life events. However, clinicians might find it useful to ask about such events, and if they are still affecting the patient, consider interventions to tackle problems related to those events (e.g. employment support, mediation, or debt advice). Further investigations of the efficacy of such interventions will be important.

The LEAF (Learning from and Engaging with Assessment and Feedback) project was funded under the Teaching Fellowship in TU Dublin, city campus for 18 months beginning in January 2018. The project team comprised 18 academics from across the TU Dublin - City Campus and there are representatives from all colleges. Also included were two further members who represented the student voice: the Director of Student Affairs and the Students’ Union Education Officer. This project sought to address a key issue in third level Teaching and Learning, that of assessment and assessment feedback. Assessment strategies have been shown to have a large impact on shaping how students learn and how they develop key employability skills. Learning from best practice nationally and internationally, and research from staff, students and quality documents, this project has developed a set of recommendations which will enhance practices in, and experiences of, assessments and feedback in TU Dublin.

Thin Clients have evolved from simple text based CRT dumb terminal devices in the 1960s, into a sophisticated architecture encompassing hardware, software, networks and protocols. However despite this recent evolution the Thin Client model has yet to re-emerge as a relevant design in an IT industry where Fat Clients (desktops and laptops) thrive. This paper describes two case studies performed within the Technological University Dublin School of Computing, focused on the issue of student acceptance of this technology. The first case study provides a dedicated single service Thin Client implementation, while the second case study adopts a process of coexistence with Fat Clients in addition to providing new services to users. This paper examines both approaches as strategies for the integration of Thin Client technology into a Higher Level Educational Institute and through our data analysis demonstrates that while co-existence improves acceptance there are clearly additional factors to be resolved.

The Context Aware Smart Classroom (CASC) is a classroom that responds to lecturers and student groups based on preset policies and the lecture timetables. CASC has been enhanced in two ways: initially to support the real-time software configuration of computers as required by specific laboratory activities; secondly to improve the decision making using knowledge engineering techniques. This paper outlines the design, implementation and evaluation of an enhanced system, CASC for Software Configuration (CASC–SC). Context aware environments respond in a pseudo-intelligent manner depending on the identity of occupants, particular location, desired activity and specific time. With the pervasive nature of personal mobile devices it is now possible to investigate development of low-cost location and identification systems that support development of a smart classroom