A 2019/2020 Summer Studentship research project
The analysis of this data will provide Nurse Maude with useful information for planning service delivery and for informing contract discussions with the District Health Board, particularly relating to client complexity.
Student: Gundu Kawale
Supervisors: Associate Professor Philippa Seaton, Gill Coe, NZICHC Research Officer, Nurse Maude
Sponsor: Nurse Maude Foundation
Nurse Maude provide district nursing services to approximately 1,500 clients per annum, with nearly 200,000 face to face client visits. In 2018, Nurse Maude introduced a new client information system (CRM) which holds all data, including number and type of visits, client demographics, referrals, admissions and discharges. This information is a rich source of data for planning service delivery.
- To analyse specific information, including demographics, number and types of visits, referral sources, case mix
- To provide a written descriptive report to Nurse Maude
Descriptive analysis of the data. Data will be provided to the student, with clear instructions about what is required. SPSS will be used as the data analysis tool.
Student researcher’s component of the study
Under supervision, the student will:
- Develop the research questions and analysis required
- Undertake a literature review (potentially in conjunction with the student undertaking the associated studentship with the Homecare Service data).
- Extract data from the CRM system
- Clean data and enter into SPSS
- Undertake data analysis
- Write the research report (including discussion and recommendations from the findings)
- Present the findings to Nurse Maude
- Draft a potential publication with UO and Nurse Maude supervisors
Nurse Maude has a strong record of supporting summer studentships and provides excellent supervision and support. The student will work closely with Nurse Maude to develop the project and will also receive supervision from the UO Centre for Postgraduate Nursing Studies to assist with the SPSS analysis component of this project.
An understanding of statistics and the ability to describe what the results mean in a written report.
Excel: Ability to sort and group large amounts of operational data (services delivered, or other operational service characteristic by population), making complex pivot tables from that data; analyse and report the results.
Statistics: prevalence, comparative ratios, trends, and other relationships between variables, statistical relevance analysis