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Dissemination & Implementation Science

(PS5-39) Waiting for Care: The Impact of Delayed Access to Therapy on Client Engagement

Saturday, November 22, 2025
10:00 AM - 11:00 AM CST
Location: Elite Hall A, Level 1
Abstract Text: Lengthy wait times for psychological treatment are among the most frequently reported barriers to care (Ofonedu et al., 2016). As psychological distress surged following the COVID-19 pandemic and high rates have persisted (Prichett et al., 2024), the growing need for therapy has further strained providers (McDonnell et al., 2022). This gap, along with the practice of extending waitlist times to manage increasing demand, has escalated into a crisis in access to mental health services. While prior research has primarily concentrated on the relationship between client-related variables (e.g., gender, ethnicity, and education level) and treatment engagement (Cade & Fidai, 2021), few studies have examined how clinic-level variables (e.g., waiting time) influence client engagement. Given that long wait times are a known barrier to care (Biringer et al., 2015), understanding their impact on engagement can help providers optimize resources, reduce no-show rates, and develop strategies to mitigate lengthy wait times, ultimately improving support for individuals affected by them. The present study examines how the amount of time spent on a waitlist for psychotherapy services at a psychology training clinic influences an individual’s likelihood of attending an initial intake appointment. Race/ethnicity, gender, and age were also examined to help identify individuals most affected by wait times, given their potential relevance to engagement. The dataset was derived from the electronic health records of 909 individuals who were placed on the adult therapy waitlist at a university-based training clinic between August 2016 and December 2024. From the initial 909 participants, a final sample of N = 768 (84%) was used in preliminary analyses after excluding cases with missing data. Given the clinic’s focus on providing low-cost, evidence-based services to the general community, the resulting sample was diverse in terms of race and ethnicity (52.7% White, 16.5% Hispanic, 13.4% Asian/Pacific Islander, 9.9% Multi-Racial, 6.9% Black, and 0.5% American Indian) and age (M = 20, SD = 13.38, Range = 18 - 99). The majority of the sample (68.9%) identified as female. Findings revealed that the average time on the waitlist ranged from 1 to 1,665 days (M = 300.65, SD = 250.74) and that 31.9% attended the intake upon being offered an appointment. A binary logistic regression analysis revealed that wait time was a significant predictor of intake attendance (odds ratio (OR) = .998, p < .001) after controlling for demographic characteristics. None of the included covariates significantly predicted intake attendance (ps > 0.05). Future analyses will use more advanced statistical methods (e.g., structural equation modeling) to better account for missing data and incorporate additional relevant covariates (e.g., history of mental health treatment, trauma exposure, income, reasons for seeking therapy). Exploratory analyses will consider the role of the COVID-19 pandemic, the onset of which involved a simultaneous increase in demand for mental health services and access to care through telehealth. Implications for the clinical and administrative management of psychotherapy waitlists will be discussed with an emphasis on how to increase access to mental health care.