91828 Evidence Based Practice in Medical Science
The Complete Study & Assessment Guide · Spring 202691828 Overview
- Level 3 subject
- Spring 2026
- Health and Medicine
- Three coursework tasks
- University of Technology Sydney
91828 Evidence Based Practice in Medical Science is a University of Technology Sydney subject that treats evidence-based practice as a complete decision process. Students begin by turning a clinical uncertainty into a focused PICO question and a reproducible search.
- One evidence cycle Question, search, appraisal, interpretation, application and evaluation are linked decisions.
- Validity before headline Allocation, blinding, follow-up and analysis determine how much confidence an estimate deserves.
- Relative needs absolute A risk ratio becomes clinically useful only when baseline risk and uncertainty are visible.
What you get in the full 91828 Study & Assessment Guide
- 12 chapters, 53 pages — every assessed topic, week by week
- 32 original practice questions, each with a model answer
- 12 worked examples, solved the way 91828 marks
- 12 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the AT1 Critical appraisal of evidence discussion forum (40%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How 91828 is assessed
| Component | Weight | Format |
|---|---|---|
| AT1 Critical appraisal of evidence discussion forum | 40% | Three discussion posts due across Weeks 6 and 7 |
| AT2 Design of a future clinical trial | 30% | Individual visual abstract and descriptive textual abstract, due Friday of Week 10 |
| AT3 Journal reflections video presentation | 30% | Individual reflective video presentation, due Friday of Week 12 |
The Spring 2026 Canvas assessment overview publishes all three weights, which sum to 100%. Its complete table contains no final-exam row. Check the live assignment pages for submission status and operational updates.
Assessment structure
Segment widths reproduce the published percentage weights in task order.
What other students are asking about 91828
- Where can I find lecture notes or study guides for 91828 at UTS?
- The 91828 study guide in AskSia Library covers Evidence Based Practice in Medical Science in 12 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in 91828?
- No final exam is listed in the offering this AskSia Library guide is built from; 91828 is assessed by AT1 Critical appraisal of evidence discussion forum 40%, AT2 Design of a future clinical trial 30%, AT3 Journal reflections video presentation 30%.
- What does 91828 cover on evidence hierarchy and study design?
- In AskSia Library's 91828 guide, the Evidence Hierarchy and Study Design chapter explains question type, design fit, validity and evidence certainty.
- What does 91828 cover on systematic reviews and meta-analysis?
- In AskSia Library's 91828 guide, the Systematic Reviews and Meta-analysis chapter explains search, selection, appraisal, heterogeneity and synthesis.
- What does 91828 cover on forest plots and funnel plots?
- In AskSia Library's 91828 guide, the Forest Plots and Funnel Plots chapter explains estimates, intervals, weights, pooled diamonds and asymmetry.
Current 91828 dates
| Date | Item | Control |
|---|---|---|
| 4, 9 and 13 September 2026 | AT1 posts | Initial, comment and response stages across Weeks 6 and 7. |
| 9 October 2026 | AT2 due | Friday of Week 10. |
| 23 October 2026 | AT3 due | Friday of Week 12. |
Dates are shown as published in the Spring 2026 Canvas assessment overview; check the live assignment pages for submission status and any operational update.
What 91828 covers
The subject follows an evidence cycle: frame a clinical question, find and rank evidence, appraise reviews and trials, interpret statistical and clinical meaning, then design and communicate a future trial. This guide follows that logic and closes with the three Spring 2026 coursework products.
Evidence Questions and PICO
EBM cycle, foreground questions, PICO and searchable conceptsEvidence based practice begins with a decision that is specific enough to search and appraise. This chapter moves through the evidence cycle and shows how population, intervention, comparator and outcome turn a broad concern into a foreground PICO question.
- Evidence based medicine cycle
- Foreground and background questions
- Population definition
- Intervention and comparator
- Patient-important outcomes
- Search concept groups
Evidence Hierarchy and Study Design
Question type, design fit, validity and evidence certaintyEvidence hierarchies are useful starting maps, not automatic quality scores. This chapter connects clinical question types with study designs, then separates the design label from execution, precision and applicability.
- Question and design fit
- Systematic reviews
- Randomised trials
- Cohort studies
- Case-control studies
- Validity and applicability
Systematic Reviews and Meta-analysis
Search, selection, appraisal, heterogeneity and synthesisA systematic review earns confidence through visible, reproducible decisions. This chapter follows the route from a registered question and comprehensive search through duplicate selection, risk-of-bias appraisal and synthesis.
- Protocol and registration
- Search completeness
- Eligibility and screening
- Risk-of-bias appraisal
- Narrative and quantitative synthesis
- Heterogeneity and sensitivity
Forest Plots and Funnel Plots
Estimates, intervals, weights, pooled diamonds and asymmetryForest plots compress effect direction, precision, study weight and a pooled estimate onto one scale. This chapter teaches a four-pass reading that begins with the measure and null, then examines individual intervals, relative weights and clinical consistency.
- Effect scale and null
- Point estimates and intervals
- Statistical weight
- Pooled diamond
- Clinical heterogeneity
- Funnel-plot asymmetry
Risk, Odds and Treatment Effects
RR, OR, RRR, ARR, NNT and baseline riskRisk and odds use different denominators, and the difference matters as events become common.
- Risk and odds denominators
- Experimental and control rates
- Risk ratio
- Relative risk reduction
- Absolute risk reduction
- Number needed to treat
P-values, Confidence and Clinical Meaning
Nulls, intervals, MCID, multiplicity and decision languageP-values describe data under a null model; they do not state the probability that a hypothesis is true or the size of an effect.
- Null hypothesis
- P-value meaning
- Confidence intervals
- Clinical importance threshold
- Multiplicity
- Equivalence and non-inferiority
Randomisation, Blinding and Bias
Sequence, concealment, performance and measurement pathwaysRandom sequence generation, allocation concealment and blinding protect different stages of a trial. This chapter follows them chronologically and shows how failures can create selection, performance or detection bias.
- Random sequence generation
- Allocation concealment
- Participant blinding
- Clinician blinding
- Outcome-assessor blinding
- Bias mechanisms
Intention-to-Treat and Analysis Choices
Analysis populations, missing outcomes, adherence and estimandsIntention-to-treat analysis preserves the comparison created by random allocation, but it does not recover outcomes that were never observed.
- Intention-to-treat principle
- Per-protocol analysis
- As-treated analysis
- Missing outcome mechanisms
- Imputation assumptions
- Sensitivity analysis
Trial Aims, Outcomes and Sample Size
Primary endpoints, target effects, power and attritionA sample-size calculation is only as defensible as the clinical question and numerical assumptions that enter it. This chapter aligns the primary aim, endpoint, target effect, variability or control event rate, alpha, power, allocation and attrition allowance.
- Primary aim and estimand
- Primary outcome
- Target effect
- Alpha and power
- Variability assumptions
- Attrition inflation
Choosing a Clinical Trial Design
Parallel, cluster, crossover, factorial and feasibility designsTrial design should solve a scientific or operational problem while preserving an interpretable estimand. This chapter compares individually randomised parallel, cluster, crossover, factorial and feasibility designs.
- Parallel design
- Cluster randomisation
- Crossover design
- Factorial design
- Contamination and carryover
- Pilot and feasibility studies
Research Ethics and Governance
Value, consent, fair selection, privacy and oversightEthical clinical research requires social value, scientific validity, fair selection, proportionate risk and meaningful consent. This chapter treats consent as an ongoing communication process and governance as the system that implements approved protections.
- Social value
- Scientific validity
- Informed consent
- Fair participant selection
- Privacy and data governance
- Safety and institutional oversight
Appraisal, Visual Abstracts and Reflection
AT1 judgement, AT2 trial communication and AT3 learningThe Spring assessments form one transferable argument. AT1 makes a calibrated evidence judgement, AT2 turns the most important uncertainty into a proposed trial, and AT3 demonstrates a change in professional reasoning or behaviour.
- AT1 appraisal judgement
- Evidence-gap statement
- AT2 visual abstract
- AT2 textual abstract
- AT3 critical reflection
- Cross-format consistency
They then distinguish study designs, appraise systematic reviews, meta-analyses and randomised controlled trials, and interpret whether an observed effect is both statistically credible and clinically meaningful. The quantitative core includes risk, odds, relative and absolute treatment effects, confidence intervals, forest plots, funnel plots and the assumptions that enter sample-size planning.
The design core separates random sequence generation from allocation concealment and blinding, examines missing data and intention-to-treat analysis, and compares parallel, cluster, crossover and factorial trial structures. Ethics and governance are integrated into the design rather than treated as an approval form at the end.
Spring 2026 is assessed entirely through coursework in the complete Canvas assessment table: AT1 is a critical appraisal discussion forum worth 40%, AT2 is the design of a future clinical trial worth 30%, and AT3 is a journal reflections video presentation worth 30%. No final-exam row appears in that table. The tasks form a deliberate chain.
AT1 asks what the current evidence supports, AT2 turns the most important remaining uncertainty into a defensible trial, and AT3 explains how the student's professional reasoning changed. The guide follows that chain, uses fresh illustrative numbers and scenarios, and keeps every interpretation attached to validity, uncertainty, patient importance and applicability.
Worked example · free
Calculate and interpret absolute and relative treatment effects
- +1EER = 12/80 = 0.15 and CER = 20/80 = 0.25.
- +2RR = 0.15/0.25 = 0.60, so RRR = 1 - 0.60 = 0.40 or 40%.
- +1ARR = 0.25 - 0.15 = 0.10 or 10 percentage points.
- +1NNT = 1/0.10 = 10 people over the trial follow-up.
- +1The result still needs interval precision, trial validity and applicability to the target population.
Key terms
- Evidence Based Practice
- Evidence based practice integrates the best available research with clinical expertise and patient circumstances for a specific decision.
- PICO
- PICO structures a clinical question by population, intervention, comparator and outcome.
- Systematic Review
- A systematic review uses explicit reproducible methods to locate, select, appraise and synthesise relevant studies.
- Meta-analysis
- Meta-analysis statistically combines compatible effect estimates using a defined weighting model.
- Risk Ratio
- A risk ratio divides event risk in one group by event risk in the comparison group.
- Odds Ratio
- An odds ratio compares event odds between groups and is not numerically identical to a risk ratio.
- Absolute Risk Reduction
- Absolute risk reduction is control risk minus intervention risk for an undesirable outcome.
- Confidence Interval
- A confidence interval displays a range of effect values compatible with the observed data and statistical model.
- Allocation Concealment
- Allocation concealment prevents recruiters from knowing the next assignment before a participant enters a trial.
- Blinding
- Blinding prevents selected trial actors from knowing assignment to reduce behavioural or measurement differences.
- Intention-to-Treat
- Intention-to-treat compares participants in their randomised groups regardless of adherence or crossover.
- Clinical Significance
- Clinical significance asks whether an effect is large enough to matter to patients or decisions.
- Statistical Power
- Statistical power is the long-run probability of rejecting the null under a specified alternative and assumptions.
- Cluster Randomisation
- Cluster randomisation allocates intact groups such as wards or clinics and requires correlation-aware analysis.
- Research Governance
- Research governance implements approved responsibilities for safety, data, quality, reporting and protocol control.
91828 FAQ
How is the subject assessed in Spring 2026?
The complete Canvas assessment table lists three coursework tasks: AT1 critical appraisal discussion forum worth 40%, AT2 design of a future clinical trial worth 30%, and AT3 journal reflections video presentation worth 30%. The numeric rows total 100%, and that table contains no final-exam row.
What does AT1 ask me to do?
AT1 asks you to synthesise appraisal of a meta-analysis, a randomised controlled trial and lower-level evidence for a clinical PICO question. Strong responses connect concrete study features to validity, effect interpretation and applicability, then communicate a bounded clinical answer rather than three disconnected checklists.
How does AT1 connect to AT2?
AT2 begins from what the collective appraisal could not resolve. A defensible proposal turns that gap into a precise aim, selects a fitting trial design, defines population, comparator, primary outcome and timing, and explains allocation, analysis, sample size and ethical protections as one aligned protocol.
What should I say about a confidence interval?
Name the effect measure, point estimate and interval, then compare the interval with the correct null and a justified threshold for clinical importance. Explain the range of plausible consequences and preserve design limitations. Do not translate an interval that crosses the null into proof of no effect.
Why report both relative and absolute effects?
Relative effects describe proportional change, while absolute effects show what that change means at the observed baseline risk. The same risk ratio can produce a large benefit in a high-risk group and a small benefit in a low-risk group, which changes NNT and decision value.
Are randomisation and allocation concealment the same?
No. Random sequence generation creates unpredictable assignments intended to balance prognosis. Allocation concealment prevents recruiters from knowing the next assignment before enrolment. A computer-generated sequence can still be subverted when envelopes, schedules or systems reveal what comes next.
Which qualities make AT3 reflection analytical?
Choose a specific decision, assumption or feedback episode. Explain your earlier view, the evidence or experience that challenged it, the revised reasoning and an observable future action. A chronological list of weekly activities describes participation but does not demonstrate professional learning or changed behaviour.
Questions students ask in each 91828 chapter
- Does every question need all four PICO elements?
- PICO is a useful structure for intervention questions, but some questions use adapted frameworks. Even within PICO, comparator or outcome terms may be omitted from the search when they make retrieval too narrow. The clinical question should still state the intended decision clearly. Chapter 01 · Evidence Questions and PICO
- Is a systematic review always the strongest evidence?
- No. Its usefulness depends on a focused question, complete search, credible included studies and a coherent synthesis. A review of biased or incompatible studies can be precisely misleading. Read its methods and evidence base rather than relying on the label. Chapter 02 · Evidence Hierarchy and Study Design
- Can a systematic review omit meta-analysis?
- Yes. A review can use systematic methods to find and appraise evidence but provide a structured narrative synthesis when studies differ too much in population, intervention, outcome or timing. Forced pooling is not a sign of greater rigour. Chapter 03 · Systematic Reviews and Meta-analysis
- What does a confidence interval crossing one mean?
- For a ratio measure, it means values compatible with the data include the null and effects on both sides, at the chosen confidence level. It does not prove equivalence or prove that treatment has no effect; the study may be imprecise. Chapter 04 · Forest Plots and Funnel Plots
- Why can an odds ratio look more extreme?
- Odds and risk have different denominators. When events are common, odds move farther from one than risk for the same event proportions, so calling an odds ratio a risk ratio can exaggerate the apparent effect. Keep the published measure name. Chapter 05 · Risk, Odds and Treatment Effects
- Is p below 0.05 the probability the result is true?
- No. A p-value is calculated assuming the null model and measures the extremeness of the data under that model. It does not provide the probability of either hypothesis, the probability of replication or the clinical importance of the effect. Chapter 06 · P-values, Confidence and Clinical Meaning
- Is alternation a random method?
- No. Alternating assignments, birth dates and record numbers are predictable. Recruiters can anticipate the next group and change who enters. A genuinely random sequence needs secure implementation so the next assignment remains unknown until enrolment is complete. Chapter 07 · Randomisation, Blinding and Bias
- Does ITT include people who never took treatment?
- Yes, if they were randomised, they remain in their assigned group for an ITT effect of assignment. Their behaviour is part of real implementation. Missing outcomes still require a transparent method and assumptions; ITT is not automatic imputation. Chapter 08 · Intention-to-Treat and Analysis Choices
- Can I choose a large effect to make the trial feasible?
- The target must be clinically important and empirically plausible. Choosing an unrealistically large effect reduces the sample but can leave the trial unable to detect a realistic meaningful benefit. Address feasibility through recruitment strategy, sites or design, not assumption inflation. Chapter 09 · Trial Aims, Outcomes and Sample Size
- When is cluster randomisation justified?
- Use it when the intervention is delivered to groups, individual allocation would create serious contamination, or policy implementation is itself the target. Ensure enough clusters, protect recruitment and adjust both sample size and analysis for within-cluster correlation. Chapter 10 · Choosing a Clinical Trial Design
- Is ethics approval enough for the whole study?
- No. Approval confirms a reviewed plan at a point in time. The team must implement consent, privacy, safety and data procedures, report deviations or unexpected risks, control amendments and maintain site governance throughout conduct and closeout. Chapter 11 · Research Ethics and Governance
- Which qualities make AT1 posts analytical?
- Make one claim, identify a concrete study feature and explain its consequence for validity, magnitude, uncertainty or applicability. Synthesise across the evidence rather than writing three sequential checklists, and close with a clinical conclusion calibrated to what the studies support. Chapter 12 · Appraisal, Visual Abstracts and Reflection
How to prepare for the assessments
Work through 91828 as one evidence chain rather than a glossary. Begin each study session with a clinical decision and write its PICO. For appraisal, use a four-column page: study feature, bias or uncertainty mechanism, likely consequence and confidence judgement.
In the quantitative chapters, preserve event counts and denominators before calculating RR, ARR or NNT, then interpret every interval against both the null and a clinical threshold. Build AT1 notes so that each paragraph has one claim, one concrete piece of evidence and one consequence.
After the discussion forum, create an evidence-gap ledger: unresolved population, intervention, outcome, timing or validity questions on the left, and possible AT2 design protections on the right. Rehearse the visual abstract by drawing the participant or cluster flow before choosing colours or icons.
For AT3, keep short records of decisions, feedback and changed actions throughout the session so the video can analyse real experiences. Use the original practice items for retrieval, cover the model answers, and verify current task instructions in Canvas before submission.
