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Conjoint Prof. Robert Gibberd

Work Phone38239
Fax38148
Email
OfficeDMB363, David Maddison Building

Research Supervision

Number of current supervisions1
Total current UoN PhD EFTSL0.4

For supervisions undertaken at an institution other that the University of Newcastle, details are shown in italics, and the institution name is listed below the program name.

Current Supervision

CommencedProposed
Completion
ProgramSupervisor TypeResearch Title
20132019PhD (CommunityMed & ClinEpid)Principal SupervisorInvestigation into the Effectiveness of Construction Interventions on the Performance of Emergency Departments Within NSW Health

Past Supervision

YearProgramSupervisor TypeResearch Title
2007M MedSc CommMed&ClinEpid[R]Sole SupervisorAHRQ Patient Safety Indicators Applied to Australian Hospital Data
2007PhD (CommunityMed & ClinEpid)Sole SupervisorThe Family as Health Producer in Indonesia: An Examination Using the Grossman Model and its Extensions
2007PhD (CommunityMed & ClinEpid)Sole SupervisorMonitoring Quality in Australian General Practice
2005PhD (Statistics)Principal SupervisorAnalysing and reporting clinical indicator data using hierarchical models
2004PhD (CommunityMed & ClinEpid)Sole SupervisorA Study of the Variation in Utilisation Rates Between Areas in NSW for 231 Procedures, 1989-1996
2004PhD (CommunityMed & ClinEpid)Principal SupervisorMeasurement and Prevention of Adverse Events in High Volume Elective Surgical Procedural Groups in an Acute Hospital Setting
2004PhD (CommunityMed & ClinEpid)Co-SupervisorCommunity-Based Evidence about the Health Care System in Rural Vietnam
2003M Sc (Statistics) [R]Principal SupervisorThe Development of a Methodology to Project Hospital Utilisation in NSW
2003PhD (CommunityMed & ClinEpid)Sole SupervisorThe Implementation of Methods for Improving District Health Management (MIDHM) and the Identification of Customer Needs and Satisfaction with the Quality of Services. A Case Study of the Jambi and the Banyumas District in Indonesia
2002PhD (Statistics)Principal SupervisorA Decision Tree Approach to Analysing Very Large Datasets