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Conflicts of Interest

Conflicts of Interest. Kim Walker – No conflicts of interest to disclose Ann Dohn – No conflicts of interest to disclose. SES032 Meaning beyond numbers: The power of qualitative inquiry for program assessment. Kim Walker, PhD Education Specialist Ann Dohn, MA DIO & GME Director.

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Conflicts of Interest

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  1. Conflicts of Interest • Kim Walker – No conflicts of interest to disclose • Ann Dohn – No conflicts of interest to disclose

  2. SES032Meaning beyond numbers:The power of qualitative inquiry for program assessment Kim Walker, PhD Education Specialist Ann Dohn, MA DIO & GME Director

  3. Session Outcomes • Describe the use and strengths of qualitative inquiry in program assessment. • Describe and perform the basic steps of qualitative inquiry including: deciding on a data collection tool, coding data for emergent themes, member checking and interviewer corroboration to establish validity. • Use emergent themes and supporting qualitative and quantitative data to provide constructive and actionable feedback to program leadership. • Compare and contrast the pros and cons of three software tools designed for qualitative inquiry.

  4. Yes, Numbers Matter!Hypothetical Institutional Report Card

  5. Qualitative Inquiry: Pros and Cons • Qualitative data = “what” “why” & “how” of the resident and faculty perceptions and experiences that drive these ratings • Explore topics in more depth and detail than quantitative research • Qualitative data, while meaning-FULL, can also be unwieldy and difficult to come to any conclusion outside of “these residents nowadays are sure an entitled bunch of needy trophy kids!” • Cannot generalize your findings to a broader population

  6. Qualitative Data Mining

  7. Data Source: Surveys • Larger populations where opinions matter and research subjects are likely to respond to closed and open questions • Important to minimize survey length and questions to only latent constructs of interest

  8. Data Source: Observations

  9. Data Source: Interviews • Interactive collection of participant perspectives. Types of interviews: • Structured (only pre-set questions) • Semi-structured • In-depth and unstructured Uses: • To clarify meaning • Discuss progress/results • Support exploratory work

  10. Data Source: Focus Groups • Combines interviewing and participant observation • Uses group interaction to generate data and gain first hand insights

  11. Focus Groups vs. In-depth Interviews Use focus groups when… Use in-depth interviews when… • Group interaction may stimulate responses • Topic amenable to less input from individual respondents • Logistically feasible to assemble target respondents in one location • Quick turn around needed, funds limited • Group/peer pressure would inhibit responses • Subject matter is too sensitive for group discussion • Topic warrants greater depth of individual respondents

  12. Recap: Sources of Qualitative Data • Surveys (GME or Program Specific) • Program Assessments (faculty and residents) • Observations (resident shadowing; rounding) • Interviews (individual or small groups) • Focus groups (larger consensus groups) • Document reviews (Internal reviews) • Case studies (holistic, multiple data sources) • Internet? Message boards? Residency chat rooms? What is the word on the “streets” about our programs?

  13. Data Collected: Now What?

  14. Data Coding: Approaching your data General  Specific Deductive Inductive Observation Specific  General Plausible explanation and recommendations Abductive

  15. Coding Qualitative Data Codes are: Labels that assign symbolic meaning to the descriptive or inferential information compiled during the study Prompts or triggers for deeper reflection Developed using multiple approaches Deductive – Provisional “start list” from conceptual framework, preliminary codebook Inductive – Codes emerge progressively, no preliminary codebook Abductive – Based on early plausible explanations, combination of deductive and inductive

  16. Tools for coding: Manual

  17. Data Coding: software supported

  18. From Text to Themes • Thorough, well documented analysis • Increases opportunities for replication • Enhances credibility • Clarify if broad categories (or domains) are theoretically or logically categorized • How findings relate to your theoretical framework

  19. Mining the Data Activity:hypothetical data set

  20. Mining the Data Activity:Step 1 – Read each excerpt and highlight key words that capture the essence of the response.

  21. Mining the Data Activity:Step 2 – Summarize in few descriptive terms in “Individual Margin Coding Notes” Interns disconnected Needs: structure, organization, attention to details Higher standards

  22. Mining the Data Activity:Step 3 – Refine coding notes into themes identified across all responses/excerpts. Interns disconnected Needs: structure, organization, attention to details Higher standards Structured, detailed curriculum

  23. Mining the Data Activity:Step 4 – Focus on key overall themes from the entire qualitative assessment.

  24. Promoting Qualitative Research Validity

  25. Promoting Qualitative Research Validity

  26. What the numbers say: Not Good!

  27. GME Plan • Qualitative Analysis of: • Resident Focus Group • Three years Program and House Staff Evaluations • Internal Review Report • Summarize Findings – Focus and Prioritize on top emergent issues • Meeting to present to program leadership • Follow up: monitor subsequent year surveys; follow up focus group?

  28. From Analysis to Recommendations Program Case Study Areas of Concern “Background Noise”

  29. From Analysis to Recommendations

  30. From Analysis to Recommendations • Program Case Study: • Focused on top three emergent themes/categories of resident dissatisfaction with learning experience, supported by quantitative and qualitative data • Lack of mentoring (research and career) • Minimal faculty engagement/ camaraderie • Service over education (lack of support staff / need for additional residents)

  31. Delivering the Golden Nuggets • Provide concise, data driven findings and recommendations • Who is receiving the information? Are they in a position to take action? • Define action plans, set goals and timelines • Check back in with leadership • Follow up with monitoring subsequent surveys; focus group/interviewees

  32. From Analysis to Recommendations • Program Case Study: • Recommendations • Lack of mentoring (research and career): • Implementation of Mentoring Program • Minimal faculty engagement/camaraderie • Focus on increase faculty attendance at educational sessions (attendance tracked) • Planned offsite retreat for faculty and residents • Service over education (lack of support staff / need for additional residents) • Hire additional support staff to handle “scut” work • Program expansion for additional residents • Next Step: Follow up focus group review and 2014 survey analysis

  33. Concise, Data-Driven Summary Applying quantitative metrics to qualitative data

  34. Qualitative Software Tools

  35. Resources

  36. Questions Please feel free to contact us with any questions @ 650-723-5948 kwalker5@stanford.edu or adohn1@stanford.edu

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