In Their Own Words: Using Open-Ended Assessment to Identify Culturally Relevant Concerns among Kenyan Adolescents

Conversational assessment using artificial intelligence is as clinically useful as depression scales and preferred by users

BACKGROUND

Depression is prevalent, chronic, and burdensome. Due to limited screening access, depression often remains undiagnosed. Artificial intelligence (AI) models based on spoken responses to interview questions may offer an effective, efficient alternative to other screening methods.

OBJECTIVE

The primary aim was to use a demographically diverse sample to validate an AI model, previously trained on human-administered interviews, on novel bot-administered interviews, and to check for algorithmic biases related to age, sex, race, and ethnicity.

METHODS

Using the Aiberry app, adults recruited via social media (N = 393) completed a brief bot-administered interview and a depression self-report form. An AI model was used to predict form scores based on interview responses alone. For all meaningful discrepancies between model inference and form score, clinicians performed a masked review to determine which one they preferred.

RESULTS

There was strong concurrent validity between the model predictions and raw self-report scores (r = 0.73, MAE = 3.3). 90 % of AI predictions either agreed with self-report or with clinical expert opinion when AI contradicted self-report. There was no differential model performance across age, sex, race, or ethnicity.

LIMITATIONS

Limitations include access restrictions (English-speaking ability and access to smartphone or computer with broadband internet) and potential self-selection of participants more favorably predisposed toward AI technology.

CONCLUSION

The Aiberry model made accurate predictions of depression severity based on remotely collected spoken responses to a bot-administered interview. This study shows promising results for the use of AI as a mental health screening tool on par with self-report measures.

Improving Mental Health on College Campuses: Perspectives of Indian College Students

The mental health of college students is increasingly viewed as an important public health priority. However, there has been little attention paid specifically to college students' perspectives on factors that contribute to mental health challenges or on potential initiatives that could address them. Even less research has focused on students in low- and middle-income countries. In an effort to better understand how to improve mental health and wellness on college campuses, we administered an open-ended survey to 141 Indian college students (M_age = 19.47, 65% female). We asked the students to identify: (a) issues that contribute to mental health problems among college students, (b) potential initiatives or strategies that could be used to improve mental health and wellness, and (c) topics that students would like to learn about in a course about mental health and wellness. Applying thematic analysis, we identified academic stressors (e.g., pressure to succeed, competitiveness) and social stressors (e.g., lack of community, party culture and substance abuse) that students reported as contributors to mental health problems. Students also described mental health promotion strategies that could be implemented by faculty members (e.g., providing academic accommodations for students with mental health concerns), the student body (e.g., establishing peer counseling groups), and individual students (e.g., checking in with others). Finally, they identified topics that they would like to learn about in mental health and wellness courses (e.g., how to identify mental health concerns, how to support friends). By raising several potential targets for mental health and wellness interventions for Indian college students, our study illustrates how open-ended surveys can be a useful and feasible way to solicit input from stakeholders in low- and middle-income countries. Future research will be needed to assess the effectiveness and feasibility of mental health promotion strategies, including those proposed by students.

Commonly Reported Problems and Coping Strategies During the COVID-19 Crisis: A Survey of Graduate and Professional Students

BACKGROUND

The COVID-19 crisis has introduced a variety of stressors, while simultaneously decreasing the availability of strategies to cope with stress. In this context, it could be useful to understand issues that people find most concerning and ways in which they cope with stress. In this study, we explored these questions with a sample of graduate and professional students.

METHOD

Using open-ended assessments, we asked participants (n = 305) to identify their biggest challenge or concern ("top problem"), their most effective way of handling stress ("effective strategy"), and their most common way of handling stress ("common strategy"). We applied thematic analysis and evaluated whether participants' strategies corresponded with evidence-based practices (EBPs).

RESULTS

Participants frequently reported top problems relating to productivity (27% of sample), physical health (26%), and emotional health (14%). Distraction was the most frequently classified common strategy (43%), whereas behavioral activation was the most frequently identified effective strategy (50%). Participants who reported a common strategy classified as an EBP reported lower depressive and anxiety symptoms. In contrast, there was no evidence of an association between symptom levels and whether or not participants' effective strategy was an EBP. Participants who reported the same strategy as both their common and effective strategy (29%) reported lower depressive symptoms than those whose common and effective strategies were different.

CONCLUSION

Our findings highlight stressors that students are experiencing and ways they are coping during the COVID-19 crisis. We discuss how these findings can inform mental health promotion efforts and future research on coping with stressors.