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Psychological Allostatic Load: the Cost of Persistence in STEM Disciplines

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Abstract

The purpose of this study is to explore the impact of persistence within STEM learning environments as a function of cumulative stress and latent trauma. The biopsychosocial impact of prolonged stressors due to hostile environments and academic demands has deleterious health effects on historically underrepresented students who enter STEM disciplines. The Trauma Symptoms Checklist for Children, clinical histories, and psychophysiomeasurement tools were used to measure the effects of cumulative stress and latent trauma as historically underrepresented students persisted through high school STEM discipline classes. Elevated responses on the inventory and history were triangulated through measures of biological markers for cumulative stress and developed into a profile combination of traits to identify those students likely to show symptomology consistent with the negative effects of cumulative stress and latent trauma. Examination of these outcomes using a latent class profile analysis model suggested the presence of cumulative stress resulting from program participation was significant.

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References

  • Adams, N. E. (2016). Dialog About Psychosocial Issues in Problem-Based Learning Sessions in Medical Education (doctoral dissertation). Retrieved from ETDA libraries Pennsylvania State University. (accession no. 13439).

  • Al-Mutawah, M. A., & Fateel, M. J. (2018). Students' achievement in math and science: How grit and attitudes influence? International Education Studies, 11(2), 97–105.

    Article  Google Scholar 

  • Annalakshmi, N., & Venkatesan, M. (2018). Perceived discrimination among students in higher education. Indian Journal of Health & Wellbeing, 9(5), 761–769.

    Google Scholar 

  • Asparouhov, T., & Muthén, B. (2012). Using Mplus TECH11 and TECH14 to test the number of latent classes. Mplus Web Notes, 14(22), 1–17.

    Google Scholar 

  • Bazelais, P., Lemay, D. J., & Doleck, T. (2016). How Does Grit Impact College Students’ Academic Achievement in Science? European Journal of Science and Mathematics Education, 4(1), 33–43.

    Article  Google Scholar 

  • Benedek, M., & Kaernbach, C. (2010). Decomposition of skin conductance data by means of nonnegative deconvolution. Psychophysiology, 47(4), 647–658.

    Google Scholar 

  • Bizik, G., Picard, M., Nijjar, R., Tourjman, V., McEwen, B. S., Lupien, S. J., & Juster, R. P. (2013). Allostatic load as a tool for monitoring physiological dysregulations and comorbidities in patients with severe mental illnesses. Harvard Review of Psychiatry, 21(6), 296–313.

    Article  Google Scholar 

  • Blair, C., & Raver, C. C. (2012). Child development in the context of adversity: Experiential canalization of brain and behavior. American Psychologist, 67(4), 309–318.

    Article  Google Scholar 

  • Bonham, V. L., Sellers, S. L., & Neighbors, H. W. (2004). John Henryism and self-reported physical health among high–socioeconomic status African American men. American Journal of Public Health, 94(5), 737–738.

    Article  Google Scholar 

  • Brown, B. A., Henderson, J. B., Gray, S., Donovan, B., Sullivan, S., Patterson, A., & Waggstaff, W. (2016). From description to explanation: An empirical exploration of the African-American pipeline problem in STEM. Journal of Research in Science Teaching, 53(1), 146–177.

    Article  Google Scholar 

  • Calarco, J. M. (2018). Negotiating opportunities: How the middle class secures advantages in school. Oxford University Press.

  • Casad, B. J., & Petzel, Z. W. (2018). Heart rate variability moderates challenge and threat reactivity to sexism among women in STEM. Social Psychology.

  • Chesmore, A. A., Weiler, L. M., & Taussig, H. N. (2017). Mentoring relationship quality and maltreated children's coping. American Journal of Community Psychology, 60(1–2), 229–241.

    Article  Google Scholar 

  • Coleman, J. S., & Coleman, J. S. (1994). Foundations of social theory. Cambridge, MA: Harvard university press.

    Google Scholar 

  • DeSarbo, W. S., Wedel, M., Vriens, M., & Ramaswamy, V. (1992). Latent class metric conjoint analysis. Marketing Letters, 3(3), 273–288.

    Article  Google Scholar 

  • Destin, M., Castillo, C., & Meissner, L. (2018). A field experiment demonstrates near peer mentorship as an effective support for student persistence. Basic and Applied Social Psychology, 40(5), 269–278.

    Article  Google Scholar 

  • Dika, S. L., & D'Amico, M. M. (2016). Early experiences and integration in the persistence of first-generation college students in STEM and non-STEM majors. Journal of Research in Science Teaching, 53(3), 368–383.

    Article  Google Scholar 

  • Douglas, L. J., Jackson, D., Woods, C., & Usher, K. (2019). Rewriting stories of trauma through peer-to-peer mentoring for and by at-risk young people. International Journal of Mental Health Nursing, 28(3), 744–756.

    Article  Google Scholar 

  • Ebenezer, J., Kaya, O. N., & Kassab, D. (2018). High school students’ reasons for their science dispositions: Community-based innovative technology-embedded environmental research projects. Research in Science Education, 1–25.

  • Franklin, J. D. (2019). Coping with racial battle fatigue: Differences and similarities for African American and Mexican American college students. Race Ethnicity and Education, 22(5), 589–609.

    Article  Google Scholar 

  • Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., & Lien, D. H. (2016). Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature, 529(7587), 509–514.

    Article  Google Scholar 

  • Habig, B., Gupta, P., Levine, B., Adams, J. (2018). An informal science education program’s impact on STEM major and STEM career outcomes. Research in Science Education, 1–24.

  • Hess, R. S., & Copeland, E. P. (2001). Students' stress, coping strategies, and school completion: A longitudinal perspective. School Psychology Quarterly, 16(4), 389–405.

    Article  Google Scholar 

  • Jeon, S., Seo, T. S., Anthony, J. C., Chung, H. (2020). Latent class analysis for repeatedly measured multiple latent class variables. Multivariate Behavioral Research, 1–15.

  • Johnson, K. M., & Mantina, N. (2016). Race influences student experience in STEM courses. The FASEB Journal, 30(1_supplement), 553–557.

    Google Scholar 

  • Johnson, S. B., & Pryce, J. M. (2013). Therapeutic mentoring: Reducing the impact of trauma for foster youth. Child Welfare, 92(3).

  • Kamata, A., Kara, Y., Patarapichayatham, C., & Lan, P. (2018). Evaluation of analysis approaches for latent class analysis with auxiliary linear growth model. Frontiers in Psychology, 9, 130.

    Article  Google Scholar 

  • Kang, J., Keinonen, T., Salonen, A. (2019). Role of interest and self-concept in predicting science aspirations: Gender study. Research in Science Education, 1–23.

  • Kim, H. G., Cheon, E. J., Bai, D. S., Lee, Y. H., & Koo, B. H. (2018). Stress and heart rate variability: A meta-analysis and review of the literature. Psychiatry Investigation, 15(3), 235–245.

    Article  Google Scholar 

  • Kirchgasler, C. (2018). True grit? Making a scientific object and pedagogical tool. American Educational Research Journal, 55(4), 693–720.

    Article  Google Scholar 

  • Lamb, R. (2014). Examination of allostasis and online laboratory simulations in a middle school science classroom. Computers in Human Behavior, 39, 224–234.

  • Lamb, R., & Premo, J. (2015). Computational modeling of teaching and learning through application of evolutionary algorithms. Computation, 3(3), 427–443.

  • Lamb, R., & Etopio, E. A. (2020). Virtual reality: a tool for preservice science teachers to put theory into practice. Journal of Science Education and Technology, 29, 573–585.

  • Lamb, R. L., Cavagnetto, A., Adesope, O. O., Yin, L., French, B., & Taylor, M. (2014). Artificially intelligent systems in education a tool for the future. Educational and Learning Games, 79.

  • Lamb, R., Annetta, L., Vallett, D., Firestone, J., Schmitter-Edgecombe, M., Walker, H., ... & Hoston, D. (2018). Psychosocial factors impacting STEM career selection. The Journal of Educational Research, 111(4), 446–458.

  • Lamb, R., Firestone, J., Schmitter-Edgecombe, M., & Hand, B. (2019). A computational model of student cognitive processes while solving a critical thinking problem in science. The Journal of Educational Research, 112(2), 243–254.

  • Lamb, R., Hand, B., & Kavner, A. (2020). Computational modeling of the effects of the science writing heuristic on student critical thinking in science using machine learning. Journal of Science Education and Technology, 1–15.

  • Law, D., Fatimilehin, I., Casale, L., Zlotowitz, S., Seymour, N., Chentite, M. M., . & Patel, W. (2018). Improving the psychological wellbeing of children and young people: Effective prevention and early intervention across health, education and social care. London: Jessica Kingsley Publishers.

    Google Scholar 

  • Lingiardi, V., & McWilliams, N. (Eds.). (2017). Psychodynamic diagnostic manual: PDM-2. Silver Springs, MD: Guilford Publications.

    Google Scholar 

  • Lissek, S., & van Meurs, B. (2015). Learning models of PTSD: Theoretical accounts and psychobiological evidence. International Journal of Psychophysiology, 98(3), 594–605.

    Article  Google Scholar 

  • Lucca, K., Horton, R., & Sommerville, J. A. (2019). Keep trying!: Parental language predicts infants’ persistence. Cognition, 193, 1–8.

    Article  Google Scholar 

  • McGee, E. O., & Martin, D. B. (2011). “You would not believe what I have to go through to prove my intellectual value!” stereotype management among academically successful Black mathematics and engineering students. American Educational Research Journal, 48(6), 1347–1389.

    Article  Google Scholar 

  • Miller, K., Sonnert, G., & Sadler, P. (2018). The influence of students’ participation in STEM competitions on their interest in STEM careers. International Journal of Science Education, Part B, 8(2), 95–114.

    Article  Google Scholar 

  • Milner IV, H. R. (2007). African American males in urban schools: No excuses—Teach and empower. Theory Into Practice, 46(3), 239–246.

    Article  Google Scholar 

  • Muthen, B. (2001). Latent variable mixture modeling. New Developments and Techniques in Structural Equation Modeling, 2, 1–33.

    Google Scholar 

  • Muthén, L. K., & Muthén, B. O. (2012). MPlus: Statistical analysis with latent variables—User’s guide. Los Angeles, CA: Muthen & Muthen.

    Google Scholar 

  • Myers, H. F., Lewis, T. T., & Parker-Dominguez, T. Y. A. N. (2003). Stress, coping and minority health. In Handbook of racial and ethnic minority psychology. Thousand Oaks, CA: Sage Publication.

    Google Scholar 

  • Nasir, N. I. S., & Vakil, S. (2017). STEM-focused academies in urban schools: Tensions and possibilities. Journal of the Learning Sciences, 26(3), 376–406.

    Article  Google Scholar 

  • Nylund-Gibson, K., & Choi, A. Y. (2018). Ten frequently asked questions about latent class analysis. Translational Issues in Psychological Science, 4(4), 440–461.

    Article  Google Scholar 

  • Oberski, D. (2016). Mixture models: Latent profile and latent class analysis. In Modern statistical methods for HCI (pp. 275–287). Cham: Springer.

    Chapter  Google Scholar 

  • Olson, J. S. (2017). Helping first-year students get grit: The impact of intentional assignments on the development of grit, tenacity, and perseverance. Journal of The First-Year Experience & Students in Transition, 29(1), 99–118.

    Google Scholar 

  • Pavlides, C., Nivón, L. G., & McEwen, B. S. (2002). Effects of chronic stress on hippocampal long-term potentiation. Hippocampus, 12(2), 245–257.

    Article  Google Scholar 

  • Rindskopf, D. (2011). The use of latent class analysis in medical diagnosis. In Recent advances in biostatistics: false discovery rates, survival analysis, and related topics (pp. 257–270).

  • Robertson-Kraft, C., & Duckworth, A. L. (2014). True grit: Trait-level perseverance and passion for long-term goals predicts effectiveness and retention among novice teachers. Teachers College Record, 116(3), 1–24.

    Article  Google Scholar 

  • Romine, W. L., & Sadler, T. D. (2016). Measuring changes in interest in science and technology at the college level in response to two instructional interventions. Research in Science Education, 46(3), 309–327.

    Article  Google Scholar 

  • Ruiz-Robledillo, N., & Moya-Albiol, L. (2015). Lower electrodermal activity to acute stress in caregivers of people with autism spectrum disorder: An adaptive habituation to stress. Journal of Autism and Developmental Disorders, 45(2), 576–588.

    Article  Google Scholar 

  • Sadowski, C. M., & Friedrich, W. N. (2000). Psychometric properties of the trauma symptom checklist for children (TSCC) with psychiatrically hospitalized adolescents. Child Maltreatment, 5(4), 364–372.

    Article  Google Scholar 

  • Saklofske, D. H., Schwean, V. L., & Reynolds, C. R. (Eds.). (2013). The Oxford handbook of child psychological assessment. New York, NY: Oxford University Press.

    Google Scholar 

  • Scaer, R. C. (2005). The trauma spectrum: Hidden wounds and human resiliency. New York, NY: WW Norton & Company.

    Google Scholar 

  • Schneider, T. R. (2004). The role of neuroticism on psychological and physiological stress responses. Journal of Experimental Social Psychology, 40(6), 795–804.

    Article  Google Scholar 

  • Secrist, M. E., Dalenberg, C. J., & Gevirtz, R. (2019). Contributing factors predicting nightmares in children: Trauma, anxiety, dissociation, and emotion regulation. Psychological Trauma: Theory, Research, Practice, and Policy, 11(1), 114.

    Article  Google Scholar 

  • Smith, S. S., Smith Carter, J., Karczewski, S., Pivarunas, B., Suffoletto, S., & Munin, A. (2015). Mediating effects of stress, weight-related issues, and depression on suicidality in college students. Journal of American College Health, 63(1), 1–12.

    Article  Google Scholar 

  • Solórzano, D. G., & Villalpando, O. (1998). Critical race theory, marginality, and the experience of students of color in higher education. Sociology of Education: Emerging Perspectives, 21, 211–222.

    Google Scholar 

  • Steele, C. M. (1998). Stereotyping and its threat are real. American Pscyhologist, 53(6), 680–681.

    Article  Google Scholar 

  • Stinson, D. W. (2008). Negotiating sociocultural discourses: The counter-storytelling of academically (and mathematically) successful African American male students. American Educational Research Journal, 45(4), 975–1010.

    Article  Google Scholar 

  • Tobin, K. (2000). Becoming an urban science educator. Research in Science Education, 30(1), 89–106.

    Article  Google Scholar 

  • Tulip, D. F., & Lucas, K. B. (1991). Persistence and withdrawal by students in a preservice science and mathematics teacher education course. Research in Science Education, 21(1), 320–327.

    Article  Google Scholar 

  • Walton, G. M., & Cohen, G. L. (2007). A question of belonging: Race, social fit, and achievement. Journal of Personality and Social Psychology, 92(1), 82–96.

    Article  Google Scholar 

  • Wherry, J. N., & Dunlop, C. E. (2018). TSCC and TSCYC screening forms in a clinical sample: Reliability, validity, and creating local clinical norms. Child Maltreatment, 23(1), 74–84.

    Article  Google Scholar 

  • Wong, B., & Chiu, Y. L. T. (2019). ‘Swallow your pride and fear’: The educational strategies of high-achieving non-traditional university students. British Journal of Sociology of Education, 40(7), 868–882.

    Article  Google Scholar 

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Authors and Affiliations

  1. East Carolina University College of Education Neurocognition Science Laboratory, Greenville, NC, USA

    Richard Lamb

  2. State University of New York College at Buffalo, Buffalo, NY, USA

    Douglas Hoston

  3. Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, Beijing, People’s Republic of China

    Jing Lin (林静)

  4. Washington State University Tricities, Richland, WA, USA

    Jonah Firestone

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  1. Richard Lamb
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  2. Douglas Hoston
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  3. Jing Lin (林静)
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Correspondence to Richard Lamb.

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Appendix. Mplus Syntax for Model Development and Input Termination Information

Appendix. Mplus Syntax for Model Development and Input Termination Information

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Lamb, R., Hoston, D., Lin (林静), J. et al. Psychological Allostatic Load: the Cost of Persistence in STEM Disciplines. Res Sci Educ 52, 1187–1206 (2022). https://doi.org/10.1007/s11165-021-10000-2

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