BackgroundTo predict and prevent mental health crises, we must develop new approaches that can provide a dramatic advance in the effectiveness, timeliness, and scalability of our interventions. However, current methods of predicting mental health crises (eg, clinical monitoring, screening) usually fail on most, if not all, of these criteria. Luckily for us, 77% of Americans carry with them an unprecedented opportunity to detect risk states and provide precise life-saving interventions. Smartphones present an opportunity to empower individuals to leverage the data they generate through their normal phone use to predict and prevent mental health crises.ObjectiveTo facilitate the collection of high-quality, passive mobile sensing data, we built the Effortless Assessment of Risk States (EARS) tool to enable the generation of predictive machine learning algorithms to solve previously intractable problems and identify risk states before they become crises.MethodsThe EARS tool captures multiple indices of a person’s social and affective behavior via their naturalistic use of a smartphone. Although other mobile data collection tools exist, the EARS tool places a unique emphasis on capturing the content as well as the form of social communication on the phone. Signals collected include facial expressions, acoustic vocal quality, natural language use, physical activity, music choice, and geographical location. Critically, the EARS tool collects these data passively, with almost no burden on the user. We programmed the EARS tool in Java for the Android mobile platform. In building the EARS tool, we concentrated on two main considerations: (1) privacy and encryption and (2) phone use impact.ResultsIn a pilot study (N=24), participants tolerated the EARS tool well, reporting minimal burden. None of the participants who completed the study reported needing to use the provided battery packs. Current testing on a range of phones indicated that the tool consumed approximately 15% of the battery over a 16-hour period. Installation of the EARS tool caused minimal change in the user interface and user experience. Once installation is completed, the only difference the user notices is the custom keyboard.ConclusionsThe EARS tool offers an innovative approach to passive mobile sensing by emphasizing the centrality of a person’s social life to their well-being. We built the EARS tool to power cutting-edge research, with the ultimate goal of leveraging individual big data to empower people and enhance mental health.

We surveyed 525 graduate students (61.7% females and 38.3% males) regarding their exposure to sexual and gender-based harassing events. Thirty-eight percent of female and 23.4% of male participants self-reported that they had experienced sexual harassment from faculty or staff; 57.7% of female and 38.8% of male participants reported they had experienced sexual harassment from other students. We explored the relation between sexual harassment and negative outcomes (trauma symptoms, campus safety, and institutional betrayal) while also considering associations with other types of victimization (sexual assault, stalking, and dating violence) during graduate school. Our results update and extend prior research on sexual harassment of graduate students; graduate-level female students continue to experience significantly more sexual harassment from faculty, staff, and students than their male counterparts, and sexual harassment is significantly associated with negative outcomes after considering other forms of victimization. Leaders in the academic community and therapists can apply these findings in their work with sexually harassed students to destigmatize the experience, validate the harm, and work toward preventing future incidents. A podcast conversation with the author of this article is available on PWQ's website at http://pwq.sagepub.com/site/misc/Index/Podcasts.xhtml

As the diagnosis and treatment of mental disorders has become increasingly medicalized (Conrad & Slodden, 2013), consideration for the relational nature of trauma has been minimized in the healing process. As psychiatrist R. D. Laing (1971) outlined in his essays, the medical model is an approach to pathology that seeks to find medical treatments for symptoms and syndromes based on categorized diagnoses. We argue that such a model implicitly locates the pathology of trauma within the individual instead of within the person(s) who perpetrated the harm or the social and societal contexts in which it took place. In this article, we argue that this framework is pathologizing insofar as it both prioritizes symptom reduction as the goal of treatment and minimizes the significance of relational harm. After providing a brief overview of betrayal trauma (Freyd, 1996) and the importance of relational processes in healing, we describe standard treatments for betrayal trauma that are grounded in the medical model. In discussing the limitations of this framework, we offer an alternative to the medicalization of trauma-related distress: relational cultural therapy (e.g., Miller& Stiver, 1997). Within this nonpathologizing framework, we highlight the importance of attending to contextual, societal, and cultural influences of trauma as well as how these influences might impact the therapeutic relationship. We then detail extratherapeutic options as additional nonpathologizing avenues for healing, as freedom to choose among a variety of options may be particularly liberating for people who have experienced trauma. Finally, we discuss the complex process of truly healing from betrayal trauma.

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