The contextual interview: a cross-cutting patient-interviewing approach for social context.

Patient interviewing pedagogy in medical education has not evolved to comprehensively capture the biopsychosocial model of healthcare delivery. While gathering a patient's social history targets important aspects of social context it does not adequately capture and account for the real-time reassessment required to understand evolving factors that influence exposure to drivers of health inequities, social determinants of health, and access to supports that promote health. The authors offer a patient interviewing approach called the Contextual Interview (CI) that specifically targets dynamic and ever-changing social context information. To substantiate the use of the CI in medical education, the authors conducted a qualitative review of the Accreditation Council for Graduate Medical Education Milestones for primary care specialties (Family Medicine, Internal Medicine, and Pediatrics). Milestones were coded to the extent to which they reflected the learner's need to acknowledge, assess, synthesize and/or apply patient contextual data in real-time patient encounters. Approximately 1 in 5 milestones met the context-related and patient-facing criteria. This milestone review further highlights the need for more intentional training in eliciting meaningful social context data during patient interviewing. The CI as a cross-cutting, practical, time-conscious, and semi-structured patient interviewing approach that deliberately elicits information to improve the clinician's sense and understanding of a patient's social context. The authors reviewed future directions in researching adapted versions of the CI for undergraduate and graduate medical education.

Clinical supervision within the primary care behavioral health model: What we know and where we need to go.

Community-based primary care settings play an important role in the provision of health care to vulnerable populations, as well as in primary care education. Much attention has been given to medical education in community-based primary care and as the need for workforce development grows for behavioral health in primary care, clinical supervision of prelicensure psychology learners in primary care will also need greater attention. Clinical supervision not only impacts learner professional development but also impacts the patient care learners provide. The purpose of this article is to give attention to clinical supervision within the primary care behavioral health (PCBH) model in the context of community-based primary care. The intersection of supervision approaches in specialty mental health care and medical education will be presented, as well as how these approaches can be combined when working with learners within PCBH. Additionally, challenges to clinical supervision in primary care will be discussed as well as solutions to improve not only workforce development in PCBH but also professional development for clinical supervisors in primary care. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Clinician's commentary to accompany "Implementing evidence-based practices in non-specialty mental health settings".

Comments on the original article by Wolk et al. (see record 2022-18591-001) regarding the implementation of evidence-based practices in non-specialty mental health settings. The authors both had tremendous gratitude for Wolk et al.'s (2022) ability to accurately describe and elucidate numerous difficulties that frontline workers experience, especially in nonspecialty mental health (SMH) settings, such as primary care where we work. With increased identification of implementation barriers, those working in non-SMH settings can mitigate these challenges via intentional strategizing upfront, considering settings' contexts (as suggested by Wolk et al.), and normalizing the need for ongoing troubleshooting. The authors appreciated the elucidation that the majority of those receiving support for mental health receive care in non-SMH settings. As much as it is imperative for those working in non-SMH settings to learn concepts illuminated from the mental health field via evidence-based (EB) practices, when it comes to reaching the masses and implementation strategies, the field might have much to learn from these non-SMH settings. When it comes to integration of siloed fields, all can benefit. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Randomized Study Using Telepresence Robots for Behavioral Health in Interprofessional Practice and Education.

Background: The events of the coronavirus disease 2019 (COVID-19) pandemic forced the world to adopt telemedicine frameworks to comply with isolation and stay-at-home regulations. Telemedicine, in various forms, has been used by patients and medical professionals for quite some time, especially telepsychiatry. To examine the efficacy and role of telesimulation as a method to educate health sciences students via telepresence robots. The study recruited students from the above health science disciplines. All participants were trained to administer a contextual interview to a standardized patient (SP) for mental health concerns. Methods: The completion of the contextual interview observation form adult (CIOF-A), National Aeronautics and Space Administration Task Load Index, self-efficacy in patient centeredness questionnaire (SEPCQ), and communication skills attitude scale with or without a telepresence robot. All participants completed baseline metrics and were trained to conduct a contextual interview to an SP. Researchers block-randomized the participants to either the telepresence robot group (TP) or in-person (IP) group. Results: The study recruited n = 43 participants to the IP group (n = 21) or TP group (n = 22). Mean participant demographics of age were 25.3 (±1.9) years in the IP group and 24.3 (±2.1) years for the TP group. Mean and standard deviation scores with effect sizes in CIOF-A scores IP: 0.05 (±1.91) and TP: -0.45 (±1.71), Cohen's d = 0.28; SEPCQ-Patient Domain scores IP: 0.42 (±4.69) and TP: 0.50 (±7.18), Cohen's d = 0.01; change in SEPCQ-Sharing Domain scores IP: 0.53 (±5.10) and TP: 0.91 (±9.98), Cohen's d = 0.05. These effect sizes will inform future studies and appropriate sample sizes. Conclusion: These data indicate that health sciences students utilizing a telepresence robot in an SP scenario to perform a behavioral health screening felt as comfortable and competent as those health sciences students performing the same behavioral health screening in person. ClinicalTrials.gov Identifier: NCT03661372.

Creating a Primary Care Workforce: Strategies for Leaders, Clinicians, and Nurses.

Many primary care clinics struggle with rapid implementation and systematic expansion of primary care behavioral health (PCBH) services. Often, an uneven course of program development is due to lack of attention to preparing clinic leadership, addressing operational factors, and training primary care providers (PCPs) and nurses. This article offers competency tools for clinic leaders, PCPs, and nurses to use in assessing their status and setting change targets. These tools were developed by researchers working to disseminate evidence-based interventions in primary care clinics that included fully integrated behavioral health consultants and were then used by early adaptors of the PCBH model. By deploying these strategies, both practicing and teaching clinics will take a big step forward in developing the primary care workforce needed for primary care teams, where the behavioral health needs of a patient of any age can be addressed at the time of need.