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ERIC Number: EJ1432982
Record Type: Journal
Publication Date: 2022-Jan
Pages: 16
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0894-587X
EISSN: EISSN-1573-3289
Predicting Mental Health Treatment Access among Adolescents with Elevated Depressive Symptoms: Machine Learning Approaches
Mallory L. Dobias; Michael B. Sugarman; Michael C. Mullarkey; Jessica L. Schleider
Administration and Policy in Mental Health and Mental Health Services Research, v49 n1 p88-103 2022
A large proportion of adolescents experiencing depression never access treatment. To increase access to effective mental health care, it is critical to understand factors associated with increased versus decreased odds of adolescent treatment access. This study used individual depression symptoms and sociodemographic variables to predict "whether" and "where" adolescents with depression accessed mental health treatments. We performed a pre-registered, secondary analysis of data from the 2017 National Survey of Drug Use and Health (NSDUH), a nationally representative sample of non-institutionalized civilians in the United States. Using four cross-validated random forest models, we predicted whether adolescents with elevated past-year depressive symptoms (N = 1,671; ages 12-17 years) accessed specific mental health treatments in the previous 12 months ("yes/no" for inpatient, outpatient, school, any). 53.38% of adolescents with elevated depressive symptoms accessed treatment of any kind. Even with depressive symptoms and sociodemographic factors included as predictors, pre-registered random forests explained < 0.00% of pseudo out-of-sample deviance in adolescent access to inpatient, outpatient, school, or overall treatments. Exploratory elastic net models explained 0.80-2.50% of pseudo out-of-sample deviance in adolescent treatment access across all four treatment types. Neither individual depressive symptoms nor any socioeconomic variables meaningfully predicted specific or overall mental health treatment access in adolescents with elevated past-year symptoms. This study highlights substantial limitations in our capacity to predict whether and where adolescents access mental health treatment and underscores the broader need for more accessible, scalable adolescent depression treatments.
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://bibliotheek.ehb.be:2123/
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A