BSTA5030: Semester 2, 2025

Units BSTA5030 Semester 2 2025 [Professional practice]

Unit outline_

BSTA5030: Professional Practice in Biostatistics

Semester 2, 2025 [Professional practice] - Camperdown/Darlington, Sydney

Overview

This unit of study is only available for students who are trainees in the NSW Ministry of Health Biostatistics Training Program. It is undertaken in the student’s final semester of enrolment to coincide with the timing of their final assessment interview. Students will prepare and submit an Evidence Portfolio for assessment, which is evaluated for both breadth and quality of evidence. This document is used to demonstrate the student’s competency acquired through coursework and applied workplace practice across the domains of biostatistical design principles, data management and analysis, professional practice, communication, and management. Successful completion of this unit demonstrates a readiness to commence a career as a professional biostatistician.

Unit details and rules

Academic unit	Public Health
Credit points	6
Prerequisites ?	60 credit points including PUBH5215 and BSTA5211
Corequisites ?	None
Prohibitions ?	BSTA5020 or BSTA5021
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Erin Cvejic, erin.cvejic@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Interactive oral	Review interview Review interview of competency evidence portfolio	0%	Ongoing	1.5 hours	AI prohibited
Outcomes assessed:
Peer or expert observation or supervision	Placement supervision reports Supervisor reports	0%	Ongoing	Variable	AI prohibited
Outcomes assessed:
Portfolio or journal	Competency Evidence Portfolio Written portfolio including reflective prefaces and associated reports	100%	Ongoing	60-80 pages	AI allowed
Outcomes assessed:
= hurdle task ?

Assessment summary

Assessment is through submission of a Competency Evidence Portfolio, which includes reflective prefaces and associated reports. For the purpose of this capstone unit enrolment, the evidence pieces will include examples of:

applying biostatistical principles to optimise the use of linked data
applying biostatistical principles to enhance the use of routinely collected surveillance data
applying biostatistical principles to the design of a study
providing high-level biostatistical advice to colleagues
preparing a dataset for use by other health professionals
leading a complex multivariable analysis
leading the analysis of data from a complex study design
reflection on the potential limitations in data relating to Aboriginal and Torres Strait Islander people

The written submission is independently reviewed by two assessors, one representing the University of Sydney and another representing the NSW Ministry of Health. The student and assessors attend a final assessment review interview, where assessors systematically discuss each tabled piece of evidence with the student to seek clarification where needed, and to provide verbal feedback. As the portfolio is assessed as Grade Only, emendations are requested if the evidence presented does not meet the expectations of assessors as reflecting competency.

Assessment criteria

Result Code	Result Name	Description
FR	Failed Requirements	When you don’t meet the learning outcomes to a satisfactory standard, for units which are marked as either Satisfied requirements or Failed requirements.
SR	Satisfied Requirements	When you meet the learning outcomes to a satisfactory standard, for units which are marked as either Satisfied requirements or Failed requirements.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Not applicable

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity	Learning outcomes
Ongoing	Preparation of Competency Evidence Portfolio for submission	Independent study (140 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Apply the knowledge and skills learned from coursework to support the design of studies and optimisation of the use of health surveillance data
LO2. Provides high-level and accurate biostatistical advice to colleagues who do not have specialised knowledge of biostatistics to support health system priorities
LO3. Efficiently manages and manipulates large administrative and health data sets incorporating analytical reasoning, relevant quality control and maintains appropriate documentation
LO4. Leads a range of complex biostatistical analyses, including of linked health data and complex study designs
LO5. Summarises and interprets the results of biostatistical analyses involving multivariable regression, and demonstrates an understanding of the limitations of the study design, data, and analytical approach

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No major changes made.

Additional information

This unit of study is only available to students who are trainees on the NSW Health Biostatistics Training Program, and who have a confirmed final assessment scheduled within the enrolled semester period.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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