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1 - 10 of 34 results for: HRP ; Currently searching winter courses. You can expand your search to include all quarters

HRP 89Q: Introduction to Cross Cultural Issues in Medicine

Preference to sophomores. Introduction to social factors that impact health care delivery, such as ethnicity, immigration, language barriers, and patient service expectations. Focus is on developing a framework to understand culturally unique and non-English speaking populations in the health care system.
Terms: Win | Units: 3 | UG Reqs: GER:EC-AmerCul
Instructors: Corso, I. (PI)

HRP 199: Undergraduate Research

Students undertake investigations sponsored by individual faculty members. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit

HRP 202: R Fundamentals for Health Research (CHPR 202)

This introductory course is a practicum in which students will learn the basics of R and use the programming language to analyze health datasets by application of classical statistical methods. A familiarity with basic descriptive and inferential statistics is required. It is assumed that students will have no (or very little) prior experience with R. Class sessions will include some lecture content and hands-on coding by each student on their own computers. Students will practice using R with open-source and simulated datasets. The primary goal of the course is to equip students with a basic and fundamental understanding of R's capabilities, experience using R with practice datasets, and the ability to extend their facility with R as their needs dictate. Students enrolled for 2 units will have additional weekly practice problems assigned.
Terms: Win | Units: 1-2
Instructors: Park, L. (PI)

HRP 206: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (CHPR 206, MED 206, STATS 211)

Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a meta-research project or reworking and evaluation of an existing published meta-analysis. Prerequisite: knowledge of basic statistics.
Terms: Win | Units: 3

HRP 208: Introduction to Concepts and Methods in Health Services and Policy Research II

Primarily for medical students in the Health Services and Policy Research scholarly concentration; continuation of 207. Topics include health economics, statistics, decision analysis, study design, quality measurement, cost benefit and effectiveness analysis, and evidence based guidelines. Recommended: 207.
Terms: Win | Units: 2

HRP 211: Law and Biosciences: Neuroscience

(Same as LAW 3006) Legal, social, and ethical issues arising from advances in neuroscience, including effects upon law and society through improvements in predicting illnesses and behaviors, reading minds through neuroimaging, understanding responsibility and consciousness, treating criminal behavior, and cognitive enhancement.
Terms: Win | Units: 3
Instructors: Greely, H. (PI)

HRP 214: Scientific Writing

Step-by-step through the process of writing and publishing a scientific manuscript. How to write effectively, concisely, and clearly in preparation of an actual scientific manuscript. Students are encouraged to bring a manuscript on which they are currently working to develop and polish throughout the course. Please note 3-units students will additionally write and revise a manuscript.
Terms: Win | Units: 2-3
Instructors: Sainani, K. (PI)

HRP 219: Evaluating Technologies for Diagnosis, Prediction and Screening

New technologies designed to monitor and improve health outcomes are constantly emerging, but most fail in the clinic and in the marketplace because relatively few are supported by reliable, reproducible evidence that they produce a health benefit. This course covers the designs and methods that should be used to evaluate technologies to diagnose patients, predict prognosis or other health events, or screen for disease. These technologies can include devices, statistical prediction rules, biomarkers, gene panels, algorithms, imaging, or any information used to predict a future or a previously unknown health state. Specific topics to be covered include the phases of test development, how to frame a proper evaluation question, measures of test accuracy, Bayes theorem, internal and external validation, prediction evaluation criteria, decision analysis, net-utility, ROC curves, c-statistics, net reclassification index, decision curves and reporting standards. Examples of technology assessm more »
New technologies designed to monitor and improve health outcomes are constantly emerging, but most fail in the clinic and in the marketplace because relatively few are supported by reliable, reproducible evidence that they produce a health benefit. This course covers the designs and methods that should be used to evaluate technologies to diagnose patients, predict prognosis or other health events, or screen for disease. These technologies can include devices, statistical prediction rules, biomarkers, gene panels, algorithms, imaging, or any information used to predict a future or a previously unknown health state. Specific topics to be covered include the phases of test development, how to frame a proper evaluation question, measures of test accuracy, Bayes theorem, internal and external validation, prediction evaluation criteria, decision analysis, net-utility, ROC curves, c-statistics, net reclassification index, decision curves and reporting standards. Examples of technology assessments and original methods papers are used. Knowledge of statistical software is not required, although facility with at least Excel for basic calculations is needed. Open to students with an understanding of introductory biostatistics, epidemiologic and clinical research study design. Undergraduates may enroll with consent of instructor.
Terms: Win | Units: 3
Instructors: Goodman, S. (PI)

HRP 224: Social Entrepreneurship and Innovation Lab (SE Lab) - Global & Planetary Health (MED 224, PUBLPOL 224)

Social Entrepreneurship and Innovation Lab (SE Lab) - Global & Planetary Health is a Collaboratory workshop for students/fellows to design and develop innovative social ventures addressing key challenges in health and the environment, especially in support of the UN Sustainable Development Goals (SDGs 2030). Your mandate in identifying problems and designing solutions is broad and flexible! SE Lab is open to students and fellows across Stanford and combines design thinking exercises, short lectures & case studies, workshops, small group teamwork, presentations, guest speakers, and faculty, practitioner and peer feedback to support you and your team in generating and developing ideas and projects that will change the world! Join SE Lab with an idea or simply the desire to join a team. Enrollment limited to 30.
Terms: Aut, Win, Spr | Units: 3-4 | Repeatable for credit
Instructors: Bloom, G. (PI)
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