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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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Emotion regulation goals: An individual difference perspective
Abstract Goals are widely understood to be central to the initiation, maintenance, and cessation of emotion regulation (ER). Recent studies have shown that there are profound individual differences in the types of ER goals people pursue and the extent to which they pursue them. Here, we highlight the importance of taking an individual difference approach to studying ER goals. First, we use the extended process model of ER to provide conceptual clarity on what ER goals are and describe the crucial role of goals in each stage of ER. We then identify five promising directions for future research using an individual difference approach to ER goals.
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- Award ID(s):
- 1808696
- PAR ID:
- 10458666
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Social and Personality Psychology Compass
- Volume:
- 13
- Issue:
- 9
- ISSN:
- 1751-9004
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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