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Primary Care Research: An Introduction (To Some Really Important Concepts)

Primary Care Research: An Introduction (To Some Really Important Concepts). John B. Schorling, M.D., M.P.H. Professor of Medicine and Public Heath Sciences James R. Martindale, Ph.D. Assistant Professor of Research and Public Health Sciences. Objectives:.

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Primary Care Research: An Introduction (To Some Really Important Concepts)

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  1. Primary Care Research: An Introduction (To Some Really Important Concepts) John B. Schorling, M.D., M.P.H. Professor of Medicine and Public Heath Sciences James R. Martindale, Ph.D. Assistant Professor of Research and Public Health Sciences

  2. Objectives: • Be able to better formulate a research question • Feel more comfortable with the clinical research process • Understand the ideas of constructs and operationalization • Understand the major differences between quantitative and qualitative approaches to doing educational research

  3. “If we knew what we were doing, it wouldn’t be called research, would it?” Albert Einstein

  4. Developing Your Question • Start with a clear purpose • Know your literature • Be iterative in your approach • Try to specify the who, what, where and when of your purpose • Ask yourself “What would the answer to this question add to the literature?” and…

  5. Developing Your Question Don’t let methodology drive the question!

  6. The Research Process • Identification of general problem/question • Literature review • Specify questions/hypotheses • Determination of design/methodology • Data collection • Data analysis/presentation • Interpretation of findings

  7. The Right and the Left • Quantitative research - numbers, numbers, numbers • Qualitative research - words, words, words

  8. Based upon the idea of “logical positivism”, that is, there is a singular reality with stable, social facts that are separate from the feelings and beliefs of individuals. Based on the notion of “constructivism”, which assumes multiple realities that are socially constructed through individual and collective perceptions or views of the same situation. Quantitative vs. Qualitative: Assumptions about the World

  9. Seeks to establish relationships and explain causes of changes in measured variables. That is, the goal of science is to explain and predict. Concern is with the understanding of the social phenomenon from the participants’ perspectives. This requires, to some degree, researcher participation. Quantitative vs. Qualitative: Research Purpose

  10. The scientific method, also known as a priori or pre-established design. Use of emergent design utilizing constant comparison and revision. Quantitative vs. Qualitative: Methods and Process

  11. Experimental or correlational designs are used to reduce error, bias and the influence of extraneous variables--control of bias is through design. Use of ethnography, which helps readers understand the multiple perspectives of the situation by the persons studied. Subjectivity in data analysis and interpretation is acknowledged. Quantitative vs. Qualitative: Prototypical Studies

  12. Detachment from study in order to avoid bias. Immersion in situation and the phenomenon being studied. Quantitative vs. Qualitative: Researcher Role

  13. Context-free generalizations Generalizations are contextually-bound. Quantitative vs. Qualitative: Context

  14. Obtained through the use of measurement and statistics Provided by detailed description of phenomenon Quantitative vs. Qualitative: Precision

  15. Results replicated by others. Extension of understandings by others. Quantitative vs. Qualitative: Verification

  16. Traditionally, parsimonious explanations were sought, but this may be changing due to technology. Summary through narrative--importance is placed on reducing complex realities to simple explanations. Quantitative vs. Qualitative: Explanation

  17. DEDUCTIVE--What’s the classic example? INDUCTIVE--anyone have an example? Quantitative vs. Qualitative: Logical Reasoning

  18. Statements of statistical probability. Tentative summary interpretations. Quantitative vs. Qualitative: Conditional Conclusions

  19. Types of Quantitative Studies • Descriptive • True experimental • Quasi-experimental • Correlational • Predictive

  20. Research Using Primary Data • Cross-sectional • Case Control • Cohort • Randomized controlled trial

  21. Cross-sectional Study • Data gathered at one point in time • Often used for surveys • Can not make inferences about causality

  22. Robert Johnston’s Project • Interested in PTSD • Reviewed literature on PTSD in primary care settings for his POM1 presentation • Decided to do a survey to determine prevalence and associated conditions • Reviewed existing instruments • Survey includes 2 PTSD questionnaires, AUDIT, SF-12 and PHQ-9 • Obtained IRB approval June, 2010 • Goal is to survey 100 patients at UMA

  23. Case Control Study • Start with the outcome- identify a sample with the condition of interest • Identify a similar control group • Look back to determine exposure • Calculate the risk in the cases and controls- odds ratio used • Can not use to establish prevalence

  24. Cohort Study • Start with an identified group • Determine exposure in everyone at the same time • Follow the group to determine who develops the outcome of interest • Can be used to determine prevalence • Association measured as relative risk (rate ratios)

  25. Randomized Controlled Trial • Gold standard for determining associations • Identify a group • Randomly assign individuals to exposure • Only reliable way to control for confounding

  26. Research Using Secondary Data • Literature review • Systematic review • Metanalysis • Analysis of existing data collected for another purpose

  27. Literature Review • Gather articles on a topic of interest • Summarize the findings

  28. Systematic Review • Gather articles using a pre-defined search strategy- may include unpublished studies • Develop a-priori objective criteria to evaluate the quality of the studies • Summarize the quality of the data and the results

  29. Metanalysis • Do a systematic review • Obtain the primary data if possible • Summarize the data quantitatively

  30. Analysis of Existing Data • Use administrative data for research • Insurance claims data- Medicare • Central Data Repository (CDR) at UVa • Use regularly collected survey data • National Center for Health Statistics performs multiple surveys periodically- NAMCS, NHIS, NMCES, NHANES • Use data collected for another study

  31. Timeline for GSP Research Project • Fall 2010- develop a research idea • Spring 2011 • Work with mentor on developing your idea • Put together a protocol for IRB approval • Summer 2011- MSSRP, collect data • Fall 2011- medical student research day • Fall 2013- research elective to finish project • Spring 2013- GSP thesis + presentation

  32. THANK YOU! Contact: jrm7e@virginia.edu

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