College student Fear of Missing Out (FoMO) and maladaptive behavior: Traditional statistical modeling and predictive analysis using machine learning - McKee, Paul C.; Budnick, Christopher J.; Walters, Kenneth S.; Antonios, Imad (PLOS ONE, 2022)
This paper reports a two-part study examining the relationship between fear of missing out (FoMO) and maladaptive behaviors in college students. This project used a cross-sectional study to examine whether college student FoMO predicts maladaptive behaviors across a range of domains (e.g., alcohol and drug use, academic misconduct, illegal behavior). Participants (N = 472) completed hard copy questionnaire packets assessing trait FoMO levels and questions pertaining to unethical and illegal behavior while in college. Part 1 utilized traditional statistical analyses (i.e., hierarchical regression modeling) to identify any relationships between FoMO, demographic variables (socioeconomic status, living situation, and gender) and the behavioral outcomes of interest. Part 2 looked to quantify the predictive power of FoMO, and demographic variables used in Part 1 through the convergent approach of supervised machine learning. Results from Part 1 indicate that college student FoMO is indeed related to many diverse maladaptive behaviors spanning the legal and illegal spectrum. Part 2, using various techniques such as recursive feature elimination (RFE) and principal component analysis (PCA) and models such as logistic regression, random forest, and Support Vector Machine (SVM), showcased the predictive power of implementing machine learning. Class membership for these behaviors (offender vs. non-offender) was predicted at rates well above baseline (e.g., 50% at baseline vs 87% accuracy for academic misconduct with just three input variables). This study demonstrated FoMO’s relationships with these behaviors as well as how machine learning can provide additional predictive insights that would not be possible through inferential statistical modeling approaches typically employed in psychology, and more broadly, the social sciences. Research in the social sciences stands to gain from regularly utilizing the more traditional statistical approaches in tandem with machine learning.
Keywords: Alcohol consumption; Behavior; Cannabis; Drug interactions; Forecasting; Machine learning; Machine learning algorithms; Principal component analysis; dept.csc; dept.psy; peer-reviewed

Empowering Young Readers: Dialogic Reading with Integrated Vocabulary Enrichment - Moore, Dina; Durwin, Cheryl C. (Rowman & Littlefield Publishers, 2022)
Keywords: dept.psy

Cognitive-Affective Styles of Biden and Trump Supporters: An Automated Text Analysis Study - Abe, Jo Ann A. (Social Psychological and Personality Science, 2022)
Are conservatives more simple-minded and happier than liberals? To revisit this question, 1,518 demographically diverse participants (52% females) were recruited from an online participant-sourcing platform and asked to write a narrative about the upcoming 2020 U.S. Presidential Election as well as complete self and candidates’ ratings of personality. The narratives were analyzed using three well-validated text analysis programs. As expected, extremely enthusiastic Trump supporters used less cognitively complex and more confident language than both their less enthusiastic counterparts and Biden supporters. Trump supporters also used more positive affective language than Biden supporters. More simplistic and categorical modes of thinking as well as positive emotional tone were also associated with positive perceptions of Trump’s, but not Biden’s personality. Dialectical complexity and positive emotional tone accounted for significant unique variance in predicting appraisals of Trump’s trustworthiness/integrity even after controlling for demographic variables, self-ratings of conscientiousness and openness, and political affiliation.
Keywords: AutoIC; FFM; LIWC; cognitive-affective styles; dept.psy; peer-reviewed; political preferences

Navigating Ethical Challenges for Pediatric Sickle Cell Pain Management in the Context of the Opioid Epidemic - Mulchan, Siddika S.; Wakefield, Emily O.; Martin, Sarah R.; Ayr-Volta, Lauren; Krenicki, Kayla; Zempsky, William T. (The Clinical Journal of Pain, 2022)
Objectives: The aim was to describe the impact of the opioid epidemic on pain management practices in pediatric sickle cell disease (SCD) and propose a conceptual framework for navigating ethical decision-making in pediatric sickle cell pain management. Methods: A review of the literature on ethical challenges in the management of sickle cell pain was conducted and considered in the context of the opioid epidemic and psychosocial factors affecting youth with SCD. The Integrated Ethical Framework for Pain Management (IEFPM) was applied to pediatric sickle cell pain management using a clinical case example. Results: Implicit bias, health-related stigma, and potential neurocognitive impairment all present unique challenges in ethical decision-making for youth with SCD. National guidelines for prescribing opioid medication may complicate providers’ clinical decision-making and affect their sickle cell pain management practices. The IEFPM was found to be applicable to ethical decision-making for pediatric sickle cell pain and captures both patient-related and provider-related aspects of clinical pain management. Discussion: The opioid epidemic has exacerbated existing ethical challenges for pain management among youth with SCD. The IEFPM provides a conceptual model that can be integrated into health care settings to facilitate ethical decision-making and promote greater health equity in the clinical management of pediatric sickle cell pain.
Keywords: Adolescent; Analgesics; Anemia; Child; Humans; Opioid; Opioid Epidemic; Pain; Pain Management; Sickle Cell; adolescent; analgesia; child; complication; dept.psy; human; narcotic analgesic agent; pain; peer-reviewed; sickle cell anemia