Men's peer support for mental health challenges: future directions for research and practice.

Peer support has a long history of helping people navigate mental health challenges and is increasingly utilized within men's mental health promotion initiatives. Despite considerable research conceptualizing and evaluating peer support in various contexts, little is known about the gendered dimensions of men's peer support and mutual help for mental health. This article provides an empirically informed commentary on men's peer support and informal help-seeking preferences to make recommendations for future directions for research and practice. Research examining men's peer support is emergent and the available evidence suggests that there is potential to conceptually align with many men's values and preferences for mental health help-seeking. Peer support offers a non-clinical, strength-based adjunct to professional support that may aid men in navigating a range of mental health challenges. Consideration must be given to the influence of gender socialization and men's diverse experiences with developing and maintaining peer relationships. It should not be assumed that authentic and supportive relationships will naturally form when men congregate together. As a growing number of interventions and programs emerge targeted at boys and men, there are important opportunities to leverage these health promotion efforts to encourage and coach men to engage in mutual help. Opportunities for research and practice are discussed to better understand and harness the health-promoting potential of peer support for men's mental health.

"I'd have no idea how to go about this " - a survey of Australian medical students' perspectives on their men's health education.

BACKGROUND: While there have been calls over the last 15 years for the inclusion of training in sex and gender-based medicine in medical school curricula and to sustain such improvements through a more gender responsive health system, little progress has been made. A related objective of the Australian National Men's Health Strategy (2020-30) is to improve practitioner core learning competencies in men's health as a critical step to reducing the burden of disease in men and disparities between men in health care access and outcomes. The aim of this study was therefore to obtain Australian medical student perspectives on the extent to which men's health and sex and gender-based medicine education is delivered in their curricula, their preparedness for engaging with men in clinical practice, and the men's health content they would have found useful during their training. METHODS: Eighty-three students (48% male) from 17 accredited medical schools, and in at least their fourth year of training, completed an online survey. The survey was co-designed by a multidisciplinary team of men's health researchers and clinicians, alongside a student representative. A mix of quantitative and qualitative survey items inquired about students' preparedness for men's health clinical practice, and coverage of men's health and sex- and gender-based medicine in their curricula. RESULTS: Most students reported minimal to no men's health coverage in their medical school education (65%). While few were offered optional men's health units (10.5%), the majority would have liked more formal training on the topic (78%). Accompanying qualitative findings substantiated a lack of preparedness among medical students to engage male patients, likely stemming from minimal coverage of men's health in their medical education. CONCLUSIONS: Australian medical students may feel underprepared for contemporary men's health clinical practice, as well as, albeit to a lesser extent, women's health clinical practice. There is a clear need and desire amongst medical students to enhance curricula with sex and gender-based medicine training.

Impact of racial microaggressions in psychotherapy vignettes with african american clients: An experimental analogue design.

Mental health researchers have focused on promoting culturally sensitive clinical care (Herman et al., 2007; Whaley & Davis, 2007), emphasizing the need to understand how biases may impact client well-being. Clients report that their therapists commit racial microaggressions-subtle, sometimes unintentional, racial slights-during treatment (Owen et al., 2014). Yet, existing studies often rely on retrospective evaluations of clients and cannot establish the causal impact of varying ambiguity of microaggressions on clients. This study uses an experimental analogue design to examine offensiveness, emotional reactions, and evaluations of the interaction across three distinct levels of microaggression statements: subtle, moderate, and overt. We recruited 158 adult African American participants and randomly assigned them to watch a brief counseling vignette. We found significant differences between the control and three microaggression statements on all outcome variables. We did not find significant differences between the microaggression conditions. This study, in conjunction with previous correlational research, highlights the detrimental impact of microaggressions within psychotherapy, regardless of racially explicit content. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Approaches to Engaging Men During Primary Healthcare Encounters: A scoping review.

Gender-responsive healthcare is critical to advancing men's health given that masculinities intersect with other social determinants to impact help-seeking, engagement with primary healthcare, and patient outcomes. A scoping review was undertaken with the aim to synthesize gender-responsive approaches used by healthcare providers (HCPs) to engage men with primary healthcare. MEDLINE, PubMed, CINAHL, and PsycINFO databases were searched for articles published between 2000 and February 2024. Titles and abstracts for 15,659 citations were reviewed, and 97 articles met the inclusion criteria. Data were extracted and analyzed thematically. Thirty-three approaches were synthesized from across counseling/psychology, general practice, social work, nursing, psychiatry, pharmacy, and unspecified primary healthcare settings. These were organized into three interrelated themes: (a) tailoring communication to reach men; (b) purposefully structuring treatment to meet men's health needs, and (c) centering the therapeutic alliance to retain men in care. Strength-based and asset-building approaches focused on reading and responding to a diversity of masculinities was reinforced across the three findings. While these approaches are recommended for the judicious integration into health practitioner education and practice, this review highlighted that the evidence remains underdeveloped, particularly for men who experience health inequities. Critical priorities for further research include intersectional considerations and operationalizing gender-responsive healthcare approaches for men and its outcomes, particularly at first point-of-contact encounters.

Improving the quality of counseling and clinical supervision in opioid treatment programs: how can technology help?

BACKGROUND: The opioid epidemic has resulted in expanded substance use treatment services and strained the clinical workforce serving people with opioid use disorder. Focusing on evidence-based counseling practices like motivational interviewing may be of interest to counselors and their supervisors, but time-intensive adherence tasks like recording and feedback are aspirational in busy community-based opioid treatment programs. The need to improve and systematize clinical training and supervision might be addressed by the growing field of machine learning and natural language-based technology, which can promote counseling skill via self- and supervisor-monitoring of counseling session recordings. METHODS: Counselors in an opioid treatment program were provided with an opportunity to use an artificial intelligence based, HIPAA compliant recording and supervision platform (Lyssn.io) to record counseling sessions. We then conducted four focus groups-two with counselors and two with supervisors-to understand the integration of technology with practice and supervision. Questions centered on the acceptability of the clinical supervision software and its potential in an OTP setting; we conducted a thematic coding of the responses. RESULTS: The clinical supervision software was experienced by counselors and clinical supervisors as beneficial to counselor training, professional development, and clinical supervision. Focus group participants reported that the clinical supervision software could help counselors learn and improve motivational interviewing skills. Counselors said that using the technology highlights the value of counseling encounters (versus paperwork). Clinical supervisors noted that the clinical supervision software could help meet national clinical supervision guidelines and local requirements. Counselors and clinical supervisors alike talked about some of the potential challenges of requiring session recording. CONCLUSIONS: Implementing evidence-based counseling practices can help the population served in OTPs; another benefit of focusing on clinical skills is to emphasize and hold up counselors' roles as worthy. Machine learning technology can have a positive impact on clinical practices among counselors and clinical supervisors in opioid treatment programs, settings whose clinical workforce continues to be challenged by the opioid epidemic. Using technology to focus on clinical skill building may enhance counselors' and clinical supervisors' overall experiences in their places of work.

Machine Learning-Based Evaluation of Suicide Risk Assessment in Crisis Counseling Calls.

OBJECTIVE: Counselor assessment of suicide risk is one key component of crisis counseling, and standards require risk assessment in every crisis counseling conversation. Efforts to increase risk assessment frequency are limited by quality improvement tools that rely on human evaluation of conversations, which is labor intensive, slow, and impossible to scale. Advances in machine learning (ML) have made possible the development of tools that can automatically and immediately detect the presence of risk assessment in crisis counseling conversations. METHODS: To train models, a coding team labeled every statement in 476 crisis counseling calls (193,257 statements) for a core element of risk assessment. The authors then fine-tuned a transformer-based ML model with the labeled data, utilizing separate training, validation, and test data sets. RESULTS: Generally, the evaluated ML model was highly consistent with human raters. For detecting any risk assessment, ML model agreement with human ratings was 98% of human interrater agreement. Across specific labels, average F1 (the harmonic mean of precision and recall) was 0.86 at the call level and 0.66 at the statement level and often varied as a result of a low base rate for some risk labels. CONCLUSIONS: ML models can reliably detect the presence of suicide risk assessment in crisis counseling conversations, presenting an opportunity to scale quality improvement efforts.

Machine-Learning-Based Prediction of Client Distress From Session Recordings.

Natural language processing (NLP) is a subfield of machine learning that may facilitate the evaluation of therapist-client interactions and provide feedback to therapists on client outcomes on a large scale. However, there have been limited studies applying NLP models to client outcome prediction that have (a) used transcripts of therapist-client interactions as direct predictors of client symptom improvement, (b) accounted for contextual linguistic complexities, and (c) used best practices in classical training and test splits in model development. Using 2,630 session recordings from 795 clients and 56 therapists, we developed NLP models that directly predicted client symptoms of a given session based on session recordings of the previous session (Spearman's rho =0.32, p<.001). Our results highlight the potential for NLP models to be implemented in outcome monitoring systems to improve quality of care. We discuss implications for future research and applications.

Automating the assessment of multicultural orientation through machine learning and natural language processing.

Recent scholarship has highlighted the value of therapists adopting a multicultural orientation (MCO) within psychotherapy. A newly developed performance-based measure of MCO capacities exists (MCO-performance task [MCO-PT]) in which therapists respond to video-based vignettes of clients sharing culturally relevant information in therapy. The MCO-PT provides scores related to the three aspects of MCO: cultural humility (i.e., adoption of a nonsuperior and other-oriented stance toward clients), cultural opportunities (i.e., seizing or making moments in session to ask about clients' cultural identities), and cultural comfort (i.e., therapists' comfort in cultural conversations). Although a promising measure, the MCO-PT relies on labor-intensive human coding. The present study evaluated the ability to automate the scoring of the MCO-PT transcripts using modern machine learning and natural language processing methods. We included a sample of 100 participants (n = 613 MCO-PT responses). Results indicated that machine learning models were able to achieve near-human reliability on the average across all domains (Spearman's ρ = .75, p < .0001) and opportunity (ρ = .81, p < .0001). Performance was less robust for cultural humility (ρ = .46, p < .001) and was poorest for cultural comfort (ρ = .41, p < .001). This suggests that we may be on the cusp of being able to develop machine learning-based training paradigms that could allow therapists opportunities for feedback and deliberate practice of some key therapist behaviors, including aspects of MCO. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Mental Health Counseling From Conversational Content With Transformer-Based Machine Learning.

Importance: Use of asynchronous text-based counseling is rapidly growing as an easy-to-access approach to behavioral health care. Similar to in-person treatment, it is challenging to reliably assess as measures of process and content do not scale. Objective: To use machine learning to evaluate clinical content and client-reported outcomes in a large sample of text-based counseling episodes of care. Design, Setting, and Participants: In this quality improvement study, participants received text-based counseling between 2014 and 2019; data analysis was conducted from September 22, 2022, to November 28, 2023. The deidentified content of messages was retained as a part of ongoing quality assurance. Treatment was asynchronous text-based counseling via an online and mobile therapy app (Talkspace). Therapists were licensed to provide mental health treatment and were either independent contractors or employees of the product company. Participants were self-referred via online sign-up and received services via their insurance or self-pay and were assigned a diagnosis from their health care professional. Exposure: All clients received counseling services from a licensed mental health clinician. Main Outcomes and Measures: The primary outcomes were client engagement in counseling (number of weeks), treatment satisfaction, and changes in client symptoms, measured via the 8-item version of Patient Health Questionnaire (PHQ-8). A previously trained, transformer-based, deep learning model automatically categorized messages into types of therapist interventions and summaries of clinical content. Results: The total sample included 166 644 clients treated by 4973 therapists (20 600 274 messages). Participating clients were predominantly female (75.23%), aged 26 to 35 years (55.4%), single (37.88%), earned a bachelor's degree (59.13%), and were White (61.8%). There was substantial variability in intervention use and treatment content across therapists. A series of mixed-effects regressions indicated that collectively, interventions and clinical content were associated with key outcomes: engagement (multiple R = 0.43), satisfaction (multiple R = 0.46), and change in PHQ-8 score (multiple R = 0.13). Conclusions and Relevance: This quality improvement study found associations between therapist interventions, clinical content, and client-reported outcomes. Consistent with traditional forms of counseling, higher amounts of supportive counseling were associated with improved outcomes. These findings suggest that machine learning-based evaluations of content may increase the scale and specificity of psychotherapy research.