Elif Dede Yildirim

Early Career Tenure-track Researcher, Auburn University

7 active projects

Young Adults and Mental Health Comorbidities

Developing one or more research questions about mental health conditions, comorbidities, and health disparities as part of my doctorate program in public health and studying as an NIH All of Us Scholar.

Scientific Questions Being Studied

Developing one or more research questions about mental health conditions, comorbidities, and health disparities as part of my doctorate program in public health and studying as an NIH All of Us Scholar.

Project Purpose(s)

  • Disease Focused Research (major depressive disorder and comorbidities with other health conditions.)
  • Population Health
  • Educational

Scientific Approaches

(To be determined with the help of my All of Us Mentor or through consultation with the faculty at the University of Texas Health Sciences Center School of Public Health.)

Anticipated Findings

(To be determined with the help of my All of Us Mentor or through consultation with the faculty at the University of Texas Health Sciences Center School of Public Health.)

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography
  • Disability Status
  • Access to Care

Data Set Used

Registered Tier

Research Team

Owner:

  • Sharon Munroe - Graduate Trainee, University of Texas Health Science Center, Houston
  • Elif Dede Yildirim - Early Career Tenure-track Researcher, Auburn University
  • Chip Shaw - Project Personnel, Texas Tech University Health Sciences Center

Duplicate of Mental Health & Surveys

Predictors of mental health across racial/ethnic groups, different SES, gender identity, sexual orientation, and geography will be examined using different methodologies including machine learning, mixture modeling, and mixed effect models.

Scientific Questions Being Studied

Predictors of mental health across racial/ethnic groups, different SES, gender identity, sexual orientation, and geography will be examined using different methodologies including machine learning, mixture modeling, and mixed effect models.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Educational
  • Methods Development

Scientific Approaches

Mental health assessments will be evaluated to examine measurement invariance across racial/ethnic groups, different SES, gender identity, sexual orientation, and geographical locations.

Anticipated Findings

Mental health assessments were shown to be invariant across different samples. The proposed study aims to assess the validity of mental health assessment among historically underrepresented populations.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

Duplicate of Mental Health & Surveys

Predictors of mental health across racial/ethnic groups, different SES, gender identity, sexual orientation, and geography will be examined using different methodologies including machine learning, mixture modeling, and mixed effect models.

Scientific Questions Being Studied

Predictors of mental health across racial/ethnic groups, different SES, gender identity, sexual orientation, and geography will be examined using different methodologies including machine learning, mixture modeling, and mixed effect models.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Educational
  • Methods Development

Scientific Approaches

Mental health assessments will be evaluated to examine measurement invariance across racial/ethnic groups, different SES, gender identity, sexual orientation, and geographical locations.

Anticipated Findings

Mental health assessments were shown to be invariant across different samples. The proposed study aims to assess the validity of mental health assessment among historically underrepresented populations.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

Mental health & COVID19

Predictors of mental health across racial/ethnic groups, different SES, gender identity, sexual orientation, and geography will be examined using different methodologies including machine learning, mixture modeling, and mixed effect models.

Scientific Questions Being Studied

Predictors of mental health across racial/ethnic groups, different SES, gender identity, sexual orientation, and geography will be examined using different methodologies including machine learning, mixture modeling, and mixed effect models.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Educational
  • Methods Development

Scientific Approaches

Mental health assessments will be evaluated to examine measurement invariance across racial/ethnic groups, different SES, gender identity, sexual orientation, and geographical locations.

Anticipated Findings

Mental health assessments were shown to be invariant across different samples. The proposed study aims to assess the validity of mental health assessment among historically underrepresented populations.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Menglin Wei - Graduate Trainee, Auburn University

Mental Health & Measurement Invariance

Predictors of mental health across racial/ethnic groups, different SES, gender identity, sexual orientation, and geography will be examined using different methodologies including machine learning, mixture modeling, and mixed effect models.

Scientific Questions Being Studied

Predictors of mental health across racial/ethnic groups, different SES, gender identity, sexual orientation, and geography will be examined using different methodologies including machine learning, mixture modeling, and mixed effect models.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Educational
  • Methods Development

Scientific Approaches

Mental health assessments will be evaluated to examine measurement invariance across racial/ethnic groups, different SES, gender identity, sexual orientation, and geographical locations.

Anticipated Findings

Mental health assessments were shown to be invariant across different samples. The proposed study aims to assess the validity of mental health assessment among historically underrepresented populations.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

Mental Health & Neighborhood Risk

The aims of this study are (a) to explore the associations between income loss, food insecurity and parents’ mental health (b) to explore the associations between social and structural neighborhood factors and parents’ mental health across racial/ethnic groups, and (c)…

Scientific Questions Being Studied

The aims of this study are (a) to explore the associations between income loss, food insecurity and parents’ mental health (b) to explore the associations between social and structural neighborhood factors and parents’ mental health across racial/ethnic groups, and (c) to explore whether the associations between food insecurity, income loss and parents’ mental health trajectories were mediated or moderated by SES and neighborhood risk across racial/ethnic groups during the COVID-19 pandemic.

Project Purpose(s)

  • Social / Behavioral

Scientific Approaches

A series of Bayesian latent growth modeling will be used to assess between person differences and within-person changes on parents' anxiety, stress, and depression levels over time. Using a step-wise approach, a series of models will be tested iteratively by increasing model complexity: random intercept, random slope, non-linear slope, and growth mixture models to determine the best fitting trajectory. Next, the impact of age, SES, and history of mental illness will be tested using conditional growth curve modeling, and growth trajectories will be compared across racial/ethnic groups by using multigroup latent growth curve models.

Anticipated Findings

Experiencing income loss or food insecurity during the COVID-19 could negatively affect the quality of parent-child interactions due to parents' increased distress. However, identifying the role of SES and neighborhood risk on the association between family financial difficulties and parents’ mental health trajectories can assist policymakers to help identify the low-resource communities where programs and services can then focus efforts to provide services to in-need families.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Mental Health & Surveys

Predictors of mental health across racial/ethnic groups, different SES, gender identity, sexual orientation, and geography will be examined using different methodologies including machine learning, mixture modeling, and mixed effect models.

Scientific Questions Being Studied

Predictors of mental health across racial/ethnic groups, different SES, gender identity, sexual orientation, and geography will be examined using different methodologies including machine learning, mixture modeling, and mixed effect models.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Educational
  • Methods Development

Scientific Approaches

Mental health assessments will be evaluated to examine measurement invariance across racial/ethnic groups, different SES, gender identity, sexual orientation, and geographical locations.

Anticipated Findings

Mental health assessments were shown to be invariant across different samples. The proposed study aims to assess the validity of mental health assessment among historically underrepresented populations.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

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