The Integrated Model of Trust offers a novel framework to interrogate the process by which diverse populations and clinical trial teams build trust. To our knowledge, this is the first model of trust-building in clinical trials that frames trust development through integrated clinical and business perspectives. By focusing on the process, rather than outcomes of trust-building diverse trial participants, clinical trials teams, participants, and cancer centers may be able to better understand, measure, and manage their trust relationships in real time. Ultimately, this may foster increased recruitment and retention of diverse populations to clinical trials.

Trust exerts a multidimensional influence at the interpersonal level in the clinical trials setting. Trust and distrust are dynamic states that are impacted, either positively or negatively, with each participant-clinical trials team interaction. Currently, accepted models of trust posit that trust and distrust coexist and their effects on engagement and retention in clinical trials are mediated by ambivalence. While understanding of trust has been informed by a robust body of work, the role of distrust and ambivalence in the trust building process are less well understood. Furthermore, the role of ambivalence and its relationship to trust and distrust in the clinical trials and oncology settings are not known. Ambivalence is a normal and uncomfortable state in the complex decision making process that characterizes the recruitment and active treatment phases of the clinical trials experience. The current review was conducted to understand the constructs of ambivalence as a mediator of trust and distrust among vulnerable, minority participants through different stages of the oncology clinical trials continuum, its triggers and the contextual factors that might influence it in the setting of minority participation in oncology clinical trials. In addition, the researchers have sought to link theory to clinical intervention by investigating the feasibility and role of Motivational Interviewing in different stages of the clinical trials continuum. Findings suggest that ambivalence can be processed and managed to enable a participant to generate a response to their ambivalence. Thus, recognizing and managing triggers of ambivalence, which include, contradictory goals, role conflicts, membership dualities, and supporting participants through the process of reducing ambivalence is critical to successfully managing trust. Contextual factors related to the totality of one's previous health-care experience, specifically among the marginalized or vulnerable, can contribute to interpersonal ambivalence. In addition, changes in information gathering as a moderator of interpersonal ambivalence may have enormous implications for gathering, assessing, and accepting health information. Finally, motivational Interviewing has widespread applications in healthcare settings, which includes enabling participants to navigate ambivalence in shared-decision making with their clinician, as well as executing changes in participant behavior. Ultimately, the Integrated Model of Trust can incorporate the role of therapeutic techniques like Motivational Interviewing in different stages of the clinical trials continuum. Ambivalence is a key component of clinical trial participation; like trust, ambivalence can be managed and plays a major role in the management of trust in interpersonal relationships over time. The management of ambivalence may play a major role in increasing clinical trial participation particularly among the marginalized or the vulnerable, who may be more susceptible to feelings of ambivalence.

Data science is merging of several techniques that include statistics, computer programming, hacking skills, and a solid expertise in specific fields, among others. This approach represents opportunities for social work research and intervention. Thus, practitioners can take advantage of data science methods and reach new standards for quality performances at different practice levels. This article addresses key terms of data science as a new set of methodologies, tools, and technologies, and discusses machine learning techniques in order to identify new skills and methodologies to support social work interventions and evidence-based practice. The challenge related to data sciences application on social work practice is the shift on the focus of interventions. Data science supports data-driven decisions to predict social issues, rather than providing an understanding of reasons for social problems. This can be both a limitation and an opportunity depending on context and needs of users and professionals.