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Could School Function Be an Adolescent Health Vital Sign? Associations Between Gradebook Data and Substance Use Among Middle School Students.

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Τίτλος: Could School Function Be an Adolescent Health Vital Sign? Associations Between Gradebook Data and Substance Use Among Middle School Students.
Συγγραφείς: DiPentino R; David Geffen School of Medicine, University of California, Los Angeles, California, USA.; Department of Pediatrics, University of Washington School of Medicine, Seattle Children's Hospital, Seattle, Washington, USA., Meza BPL; Division of General Internal Medicine and Health Services Research, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA., Jackson N; Division of General Internal Medicine and Health Services Research, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA., Dosanjh KK; Division of General Internal Medicine and Health Services Research, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA., Dudovitz RN; Division of General Pediatrics, Department of Pediatrics and Children's Discovery and Innovation Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA., Wong MD; Division of General Internal Medicine and Health Services Research, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Πηγή: The Journal of school health [J Sch Health] 2026 Oct; Vol. 96 (10), pp. e70261.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley Country of Publication: United States NLM ID: 0376370 Publication Model: Print Cited Medium: Internet ISSN: 1746-1561 (Electronic) Linking ISSN: 00224391 NLM ISO Abbreviation: J Sch Health Subsets: MEDLINE
Imprint Name(s): Publication: Nov. 2008- : Hoboken, NJ : Wiley
Original Publication: Columbus, Ohio : American Association of School Physicians
Ιατρικοί όροι (MeSH): Substance-Related Disorders*/epidemiology , Students*/statistics & numerical data , Students*/psychology , Adolescent Behavior*/psychology , Schools* , Adolescent Health*, Los Angeles/epidemiology ; Humans ; Female ; Adolescent ; Male ; Child
Περίληψη: Background: Identifying early substance use and at-risk adolescents is a challenge for public health. The objective was to identify variables in digital school records that might distinguish between youth at high and low risk of adolescent substance use.
Methods: We performed a secondary analysis of sixth-grade students followed annually through eighth grade in Los Angeles, California. The composite outcome was self-reported lifetime use of alcohol, cannabis, or other substances by eighth grade. Independent variables tested were parent-reported GPA, transcript-recorded GPA, and gradebook data including summative (quizzes and tests), formative (homework), and engagement (classroom behavior and participation) data. We evaluated models for discriminative ability.
Results: Of the 234 students included in our sample, 51.7% were female, 82.1% were Latino, and 47 reported the composite outcome. Gradebook formative and engagement scores were most consistently associated with substance use. A model including gradebook engagement score and female sex demonstrated moderate ability to discriminate (c-statistic = 0.79).
Implications for School Health Policy, Practice, and Equity: Findings support the potential for digital gradebook data to serve as an inexpensive, readily available, and moderately accurate risk indicator of adolescent substance use.
Conclusions: Schools, families, and health systems may leverage increasingly digitized school data to identify and support youth even where traditional modes of screening are limited.
(© 2026 The Author(s). Journal of School Health published by Wiley Periodicals LLC on behalf of American School Health Association.)
References: M. L. Allen, D. Garcia‐Huidobro, C. Porta, et al., “Effective Parenting Interventions to Reduce Youth Substance Use: A Systematic Review,” Pediatrics 138, no. 2 (2016): e20154425, https://doi.org/10.1542/peds.2015‐4425.
L. L. Hill, M. Hovell, E. Blumberg, et al., “Gaps Between Adolescent Risk Behaviors and Disclosure During Outpatient Visits,” International Journal of Family Medicine 2013 (2013): 718568, https://doi.org/10.1155/2013/718568.
C. M. Rand and N. P. N. Goldstein, “Patterns of Primary Care Physician Visits for US Adolescents in 2014: Implications for Vaccination,” Academic Pediatrics 18, no. 2, Supplement (2018): S72–S78, https://doi.org/10.1016/j.acap.2018.01.002.
US Preventive Services Task Force, “Screening for Unhealthy Drug Use: US Preventive Services Task Force Recommendation Statement,” Journal of the American Medical Association 323, no. 22 (2020): 2301–2309, https://doi.org/10.1001/jama.2020.8020.
K. L. Henry, “Academic Achievement and Adolescent Drug Use: An Examination of Reciprocal Effects and Correlated Growth Trajectories,” Journal of School Health 80, no. 1 (2010): 38–43, https://doi.org/10.1111/j.1746‐1561.2009.00455.x.
J. G. Bachman, The Education‐Drug Use Connection: How Successes and Failures in School Relate to Adolescent Smoking, Drinking, Drug Use, and Delinquency (Psychology Press, 2008).
R. Rodericks, U. Vu, J. R. Holmes, J. Ryan, T. Sentell, and S. Saka, “Insights in Public Health,” Hawaii Journal of Medicine & Public Health 77, no. 11 (2018): 297–304, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6218687/.
C. Huỳnh, A. J. S. Morin, J. S. Fallu, J. Maguire‐L., A. Descheneaux‐Buffoni, and M. Janosz, “Unpacking the Longitudinal Associations Between the Frequency of Substance Use, Substance Use Related Problems, and Academic Achievement Among Adolescents,” Journal of Youth and Adolescence 48, no. 7 (2019): 1327–1341, https://doi.org/10.1007/s10964‐019‐01038‐7.
W. D. Crano, J. T. Siegel, E. M. Alvaro, A. Lac, and V. Hemovich, “The At‐Risk Adolescent Marijuana Nonuser: Expanding the Standard Distinction,” Prevention Science 9, no. 2 (2008): 129–137, https://doi.org/10.1007/s11121‐008‐0090‐0.
T. T. Clark, F. Z. Belgrave, and A. Nasim, “Risk and Protective Factors for Substance Use Among Urban African American Adolescents Considered High‐Risk,” Journal of Ethnicity in Substance Abuse 7, no. 3 (2008): 292–303, https://doi.org/10.1080/15332640802313296.
W. H. Jeynes, “The Relationship Between the Consumption of Various Drugs by Adolescents and Their Academic Achievement,” American Journal of Drug and Alcohol Abuse 28, no. 1 (2002): 15–35, https://doi.org/10.1081/ADA‐120001279.
B. A. Bugbee, K. H. Beck, C. S. Fryer, and A. M. Arria, “Substance Use, Academic Performance, and Academic Engagement Among High School Seniors,” Journal of School Health 89, no. 2 (2019): 145–156, https://doi.org/10.1111/josh.12723.
M. W. Arthur, E. C. Brown, J. S. Briney, et al., “Examination of Substance Use, Risk Factors, and Protective Factors on Student Academic Test Score Performance,” Journal of School Health 85, no. 8 (2015): 497–507, https://doi.org/10.1111/josh.12279.
K. L. Henry, K. E. Knight, and T. P. Thornberry, “School Disengagement as a Predictor of Dropout, Delinquency, and Problem Substance Use During Adolescence and Early Adulthood,” Journal of Youth and Adolescence 41, no. 2 (2012): 156–166, https://doi.org/10.1007/s10964‐011‐9665‐3.
M. H. Afzali, M. Sunderland, S. Stewart, et al., “Machine‐Learning Prediction of Adolescent Alcohol Use: A Cross‐Study, Cross‐Cultural Validation,” Addiction 114, no. 4 (2019): 662–671, https://doi.org/10.1111/add.14504.
S. Kim, H. Kim, S. Kim, et al., “Machine Learning–Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study,” Journal of Medical Internet Research 27, no. 1 (2025): e62805, https://doi.org/10.2196/62805.
F. W. Chi, S. Alexeeff, B. Ahmedani, et al., “Predicting Adolescent Alcohol and Other Drug Problems Using Electronic Health Records Data,” Journal of Substance Abuse Treatment 132 (2022): 108487, https://doi.org/10.1016/j.jsat.2021.108487.
B. J. Zimmerman and A. Kitsantas, “Homework Practices and Academic Achievement: The Mediating Role of Self‐Efficacy and Perceived Responsibility Beliefs,” Contemporary Educational Psychology 30, no. 4 (2005): 397–417, https://doi.org/10.1016/j.cedpsych.2005.05.003.
H. Cooper and J. C. Valentine, “Using Research to Answer Practical Questions About Homework,” Educational Psychologist 36, no. 3 (2001): 143–153, https://doi.org/10.1207/S15326985EP3603_1.
U. Trautwein, I. Schnyder, A. Niggli, M. Neumann, and O. Lüdtke, “Chameleon Effects in Homework Research: The Homework–Achievement Association Depends on the Measures Used and the Level of Analysis Chosen,” Contemporary Educational Psychology 34, no. 1 (2009): 77–88, https://doi.org/10.1016/j.cedpsych.2008.09.001.
E. L. Vockell and W. Asher, “Sources of Information in Educational Research Literature,” Journal of Experimental Education 58, no. 1 (1989): 63–70, https://doi.org/10.1080/00220973.1989.10806523.
E. L. Vockell and D. J. Fiore, “Electronic Gradebooks: What Current Programs Can Do for Teachers,” Clearing House: A Journal of Educational Strategies, Issues and Ideas 66, no. 3 (1993): 141–145, https://doi.org/10.1080/00098655.1993.9955953.
P. Bergman, R. N. Dudovitz, K. K. Dosanjh, and M. D. Wong, “Engaging Parents to Prevent Adolescent Substance Use: A Randomized Controlled Trial,” American Journal of Public Health 109, no. 10 (2019): 1455–1461, https://doi.org/10.2105/AJPH.2019.305240.
D. M. Wong, “Linking Families Together Study—A Randomized Trial to Raise Parental Monitoring,” clinicaltrials.gov, 2017, accessed July 3, 2025, https://clinicaltrials.gov/study/NCT02129153.
E. Gallegos and D. J. Willis, “Most California High School Seniors Shut Out of Even Applying to the State's Universities,” EdSource, 2024, accessed July 24, 2024, https://edsource.org/2024/most‐california‐high‐school‐seniors‐shut‐out‐of‐even‐applying‐to‐the‐states‐universities/705635.
National Center for Education Statistics, Concentration of Public School Students Eligible for Free or Reduced‐Price Lunch (U.S. Department of Education, Institute of Education Sciences, 2024).
C. G. Casey, “Youth Risk Behavior Surveillance—United States, 2019,” Morbidity and Mortality Weekly Report 69, no. 1 (2020): 88.
M. A. Luque‐Fernandez, D. Redondo‐Sánchez, and C. Maringe, “Cvauroc: Command to Compute Cross‐Validated Area Under the Curve for ROC Analysis After Predictive Modeling for Binary Outcomes,” Stata Journal 19, no. 3 (2019): 615–625, https://doi.org/10.1177/1536867X19874237.
M. Psyridou, F. Prezja, M. Torppa, M. K. Lerkkanen, A. M. Poikkeus, and K. Vasalampi, “Machine Learning Predicts Upper Secondary Education Dropout as Early as the End of Primary School,” Scientific Reports 14, no. 1 (2024): 12956, https://doi.org/10.1038/s41598‐024‐63629‐0.
D. Sansone, “Beyond Early Warning Indicators: High School Dropout and Machine Learning,” Oxford Bulletin of Economics and Statistics 81, no. 2 (2019): 456–485, https://doi.org/10.1111/obes.12277.
B. J. Bradley and A. C. Greene, “Do Health and Education Agencies in the United States Share Responsibility for Academic Achievement and Health? A Review of 25 Years of Evidence About the Relationship of Adolescents' Academic Achievement and Health Behaviors,” Journal of Adolescent Health 52, no. 5 (2013): 523–532, https://doi.org/10.1016/j.jadohealth.2013.01.008.
R. G. Cox, L. Zhang, W. D. Johnson, and D. R. Bender, “Academic Performance and Substance Use: Findings From a State Survey of Public High School Students,” Journal of School Health 77, no. 3 (2007): 109–115, https://doi.org/10.1111/j.1746‐1561.2007.00179.x.
M. Haller, E. Handley, L. Chassin, and K. Bountress, “Developmental Cascades: Linking Adolescent Substance Use, Affiliation With Substance Use Promoting Peers, and Academic Achievement to Adult Substance Use Disorders,” Development and Psychopathology 22, no. 4 (2010): 899–916, https://doi.org/10.1017/S0954579410000532.
A. L. Bryant, J. E. Schulenberg, P. M. O'Malley, J. G. Bachman, and L. D. Johnston, “How Academic Achievement, Attitudes, and Behaviors Relate to the Course of Substance Use During Adolescence: A 6‐Year, Multiwave National Longitudinal Study,” Journal of Research on Adolescence 13, no. 3 (2003): 361–397, https://doi.org/10.1111/1532‐7795.1303005.
B. V. Dever, J. E. Schulenberg, J. B. Dworkin, P. M. O'Malley, D. D. Kloska, and J. G. Bachman, “Predicting Risk‐Taking With and Without Substance Use: The Effects of Parental Monitoring, School Bonding, and Sports Participation,” Prevention Science 13, no. 6 (2012): 605–615, https://doi.org/10.1007/s11121‐012‐0288‐z.
M. O. Edelen, D. F. McCaffrey, P. L. Ellickson, J. S. Tucker, and D. J. Klein, “Creating a Developmentally Sensitive Measure of Adolescent Alcohol Misuse: An Application of Item Response Theory,” Substance Use & Misuse 44, no. 6 (2009): 835–847, https://doi.org/10.1080/10826080802484686.
B. P. L. Meza, R. N. Dudovitz, Z. D. Cooper, J. S. Tucker, and M. D. Wong, “Adolescent Cannabis Misuse Scale: Longitudinal Associations With Substance Use, Mental Health, and Social Determinants of Health in Early Adulthood,” Substance Use & Misuse 58, no. 9 (2023): 1080–1089, https://doi.org/10.1080/10826084.2023.2201852.
Y. Li, W. Zhang, J. Liu, et al., “The Role of School Engagement in Preventing Adolescent Delinquency and Substance Use: A Survival Analysis,” Journal of Adolescence 34, no. 6 (2011): 1181–1192, https://doi.org/10.1016/j.adolescence.2011.07.003.
L. M. Lee, Principles & Practice of Public Health Surveillance, 3rd ed. (Oxford University Press, 2010).
M. Jellinek and J. M. Murphy, “Screening for Psychosocial Functioning as the Eighth Vital Sign,” JAMA Pediatrics 175, no. 1 (2021): 13–14, https://doi.org/10.1001/jamapediatrics.2020.2005.
J. D. Nordin, L. I. Solberg, and E. D. Parker, “Adolescent Primary Care Visit Patterns,” Annals of Family Medicine 8, no. 6 (2010): 511–516, https://doi.org/10.1370/afm.1188.
K. G. M. Moons, J. A. A. Damen, T. Kaul, et al., “PROBAST+AI: An Updated Quality, Risk of Bias, and Applicability Assessment Tool for Prediction Models Using Regression or Artificial Intelligence Methods,” BMJ 388 (2025): e082505, https://doi.org/10.1136/bmj‐2024‐082505.
FERPA, “Protecting Student Privacy,” accessed June 1, 2026, https://studentprivacy.ed.gov/ferpa.
A. Baskin‐Sommers and I. Sommers, “The Co‐Occurrence of Substance Use and High‐Risk Behaviors,” Journal of Adolescent Health 38, no. 5 (2006): 609–611, https://doi.org/10.1016/j.jadohealth.2005.07.010.
Grant Information: KL2 TR001882 United States TR NCATS NIH HHS; Life Course Intervention Research Network; UA6MC32492 Health Resources and Services Administration of the US Department of Health and Human Services; KL2TR001882 United States NH NIH HHS
Contributed Indexing: Keywords: academic performance; adolescence; adolescent substance use; alcohol use; cannabis use; risk factors
Entry Date(s): Date Created: 20261011 Date Completed: 20261011 Latest Revision: 20261013
Update Code: 20261014
PubMed Central ID: PMC13654624
DOI: 10.1111/josh.70261
PMID: 42860269
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1746-1561
DOI:10.1111/josh.70261