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  1. Abstract

    Growing evidence indicates that extreme environmental conditions in summer months have an adverse impact on mental and behavioral disorders (MBD), but there is limited research looking at youth populations. The objective of this study was to apply machine learning approaches to identify key variables that predict MBD‐related emergency room (ER) visits in youths in select North Carolina cities among adolescent populations. Daily MBD‐related ER visits, which totaled over 42,000 records, were paired with daily environmental conditions, as well as sociodemographic variables to determine if certain conditions lead to higher vulnerability to exacerbated mental health disorders. Four machine learning models (i.e., generalized linear model, generalized additive model, extreme gradient boosting, random forest) were used to assess the predictive performance of multiple environmental and sociodemographic variables on MBD‐related ER visits for all cities. The best‐performing machine learning model was then applied to each of the six individual cities. As a subanalysis, a distributed lag nonlinear model was used to confirm results. In the all cities scenario, sociodemographic variables contributed the greatest to the overall MBD prediction. In the individual cities scenario, four cities had a 24‐hr difference in the maximum temperature, and two of the cities had a 24‐hr difference in the minimum temperature, maximum temperature, or Normalized Difference Vegetation Index as a leading predictor of MBD ER visits. Results can inform the use of machine learning models for predicting MBD during high‐temperature events and identify variables that affect youth MBD responses during these events.

     
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  2. null (Ed.)
    Underrepresented minority (URM) students have not been well represented within the ranks of doctoral degree holders or faculty in STEM disciplines despite the increased attention in recent years to this concern. URM students lag considerably behind White and Asian students in degree completion and faculty appointments. One intervention widely touted as effective in promoting positive outcomes is mentoring however URM students often lack access to mentoring and just as importantly mentors lack culturally responsive knowledge, skills and dispositions required to be effective mentors to URM students. A qualitative study was conducted to better understand how the knowledge, skills and dispositions of STEM faculty align with culturally responsive mentoring. Three themes were constructed from the data: role ambiguity, preparedness, and culture of doing. The study concluded that faculty need more and better training around cultural responsiveness to meet the needs of URM students. 
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  3. null (Ed.)
    Underrepresented minority (URM) students have not been well represented within the ranks of doctoral degree holders or faculty in STEM disciplines despite the increased attention in recent years to this concern. URM students lag considerably behind White and Asian students in degree completion and faculty appointments. One intervention widely touted as effective in promoting positive outcomes is mentoring however URM students often lack access to mentoring and just as importantly mentors lack culturally responsive knowledge, skills and dispositions required to be effective mentors to URM students. A qualitative study was conducted to better understand how the knowledge, skills and dispositions of STEM faculty align with culturally responsive mentoring. Three themes were constructed from the data: role ambiguity, preparedness, and culture of doing. The study concluded that faculty need more and better training around cultural responsiveness to meet the needs of URM students. 
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