BCBR syllabus — all 23 lectures
The course runs twenty-three lectures, from what health research is through to publication ethics. BCBR Buddy carries 30 practice questions for every one of them — 690 in total. The first 5 lectures are free.
The order below is the course’s own sequence, which is worth keeping: the later lectures assume the vocabulary of the earlier ones. If you are short on time, note that lectures 6, 8, 11 and 12 carry a disproportionate amount of what the exam asks about.
| # | Lecture | Questions |
|---|---|---|
| 1 | Introduction to health researchFree What counts as health research, and the three reviews every study faces: scientific, ethics, regulatory. | 30 |
| 2 | Formulating a research questionFree The life cycle of research, and the verbs that separate a descriptive objective from an analytical one. | 30 |
| 3 | Literature reviewFree Information seeking, retrieval, critical appraisal and systematic review — four terms this lecture recycles relentlessly. | 30 |
| 4 | Measures of disease frequencyFree Incidence versus prevalence, rates versus ratios versus proportions. | 30 |
| 5 | Descriptive study designsFree Case reports, case series, cross-sectional and ecological studies, and what each can and cannot show. | 30 |
| 6 | Analytical study designs Cohort and case-control: direction of enquiry, odds ratio versus relative risk. | 30 |
| 7 | Experimental study designs: Clinical trials Randomisation, blinding, allocation concealment, and trial phases. | 30 |
| 8 | Validity of epidemiological studies Selection bias, information bias, confounding — and the fact that confounding is fixable while bias is not. | 30 |
| 9 | Qualitative research methods In-depth interviews, focus groups, saturation, and when numbers are the wrong tool. | 30 |
| 10 | Measurement of study variables Scales of measurement, validity versus reliability, sensitivity versus specificity. | 30 |
| 11 | Sampling methods Probability versus non-probability, and why convenience sampling costs you generalisability. | 30 |
| 12 | Calculating sample size and power Alpha, beta, power, effect size, and what actually drives the number up or down. | 30 |
| 13 | Selection of study population Target, accessible and study populations; inclusion and exclusion criteria. | 30 |
| 14 | Study plan and project management Time, cost and quality — the triangle that defines project failure. | 30 |
| 15 | Designing data collection tools Questionnaire construction, question order, pre-testing and pilot studies. | 30 |
| 16 | Principles of data collection Training collectors, supervision, and the sources of error that creep in during fieldwork. | 30 |
| 17 | Data management Entry, cleaning, validation, double entry, and the audit trail. | 30 |
| 18 | Overview of data analysis Descriptive and inferential statistics, choosing a test, p-values and confidence intervals. | 30 |
| 19 | Ethical framework for health research Autonomy, beneficence, non-maleficence, justice; informed consent and vulnerable groups. | 30 |
| 20 | Conducting clinical trials GCP, regulatory approvals, adverse event reporting, and trial registration. | 30 |
| 21 | Preparing a concept paper Turning a question into a fundable one-page argument. | 30 |
| 22 | Elements of a protocol Every section a protocol must contain, and what belongs in each. | 30 |
| 23 | Publication ethics Authorship criteria, plagiarism, redundant publication, conflict of interest. | 30 |
How to work through it
Lecture by lecture, in order, answering rather than reading. Get a lecture’s thirty questions to the point where nothing in them surprises you, then move on. Come back to your mistakes — the app collects every question you got wrong into one place for exactly that.
If your exam date is close, set it in the app and it will work backwards into a daily target across whatever time you actually have left.