Enacting 'more-than-human' care: Clients' and counsellors' views on the multiple affordances of chatbots in alcohol and other drug counselling

Digital Mental Health: Role of Artificial Intelligence in Psychotherapy

Background

It is estimated that 6%-7% of the population suffers from mental disorders. WHO reported that one in four families is likely to have at least one member with a behavioural or mental disorder. Post-pandemic, the world has experienced a huge surge in mental health issues. Unfortunately, not everyone is able to access the available mental health services due to constraints such as lack of financial assistance, living in remote areas, fear of being stigmatised and lack of awareness. The emergence of online mental health services could solve some of these problems, as these are easily accessible to people from anywhere, are cost effective and also reduce the fear of being judged or labelled. Lots of efforts are being made today to integrate artificial intelligence with the traditional form of psychotherapy. The role of chatbots for mental health services in the form of e-therapies has been found to be highly relevant and important.

Summary

The present study aims to systematically review the evidence on the use of AI-based methods for treating mental health issues. Overall, 95 studies were extracted using some of the popular databases such as Mendeley, PubM, INFO and DOAJ. The terms used in the search included 'psychotherapy', 'online therapies', 'artificial intelligence' and 'online counselling'. Finally, after screening, 13 studies were selected based on the eligibility criteria. Most of these studies had employed conversational agents as an intervention. The results obtained showed the significant positive consequences of using AI-based approaches in treating mental health issues.

Key Message

The study strongly suggests integrating AI with the traditional form of counselling.

Public Perception on Artificial Intelligence-Driven Mental Health Interventions: Survey Research

BACKGROUND

Artificial intelligence (AI) has become increasingly important in health care, generating both curiosity and concern. With a doctor-patient ratio of 1:834 in India, AI has the potential to alleviate a significant health care burden. Public perception plays a crucial role in shaping attitudes that can facilitate the adoption of new technologies. Similarly, the acceptance of AI-driven mental health interventions is crucial in determining their effectiveness and widespread adoption. Therefore, it is essential to study public perceptions and usage of existing AI-driven mental health interventions by exploring user experiences and opinions on their future applicability, particularly in comparison to traditional, human-based interventions.

OBJECTIVE

This study aims to explore the use, perception, and acceptance of AI-driven mental health interventions in comparison to traditional, human-based interventions.

METHODS

A total of 466 adult participants from India voluntarily completed a 30-item web-based survey on the use and perception of AI-based mental health interventions between November and December 2023.

RESULTS

Of the 466 respondents, only 163 (35%) had ever consulted a mental health professional. Additionally, 305 (65.5%) reported very low knowledge of AI-driven interventions. In terms of trust, 247 (53%) expressed a moderate level of Trust in AI-Driven Mental Health Interventions, while only 24 (5.2%) reported a high level of trust. By contrast, 114 (24.5%) reported high trust and 309 (66.3%) reported moderate Trust in Human-Based Mental Health Interventions; 242 (51.9%) participants reported a high level of stigma associated with using human-based interventions, compared with only 50 (10.7%) who expressed concerns about stigma related to AI-driven interventions. Additionally, 162 (34.8%) expressed a positive outlook toward the future use and social acceptance of AI-based interventions. The majority of respondents indicated that AI could be a useful option for providing general mental health tips and conducting initial assessments. The key benefits of AI highlighted by participants were accessibility, cost-effectiveness, 24/7 availability, and reduced stigma. Major concerns included data privacy, security, the lack of human touch, and the potential for misdiagnosis.

CONCLUSIONS

There is a general lack of awareness about AI-driven mental health interventions. However, AI shows potential as a viable option for prevention, primary assessment, and ongoing mental health maintenance. Currently, people tend to trust traditional mental health practices more. Stigma remains a significant barrier to accessing traditional mental health services. Currently, the human touch remains an indispensable aspect of human-based mental health care, one that AI cannot replace. However, integrating AI with human mental health professionals is seen as a compelling model. AI is positively perceived in terms of accessibility, availability, and destigmatization. Knowledge and perceived trustworthiness are key factors influencing the acceptance and effectiveness of AI-driven mental health interventions.

Health Professionals' Views on the Use of Conversational Agents for Health Care: Qualitative Descriptive Study

BACKGROUND

In recent years, there has been an increase in the use of conversational agents for health promotion and service delivery. To date, health professionals' views on the use of this technology have received limited attention in the literature.

OBJECTIVE

The purpose of this study was to gain a better understanding of how health professionals view the use of conversational agents for health care.

METHODS

Physicians, nurses, and regulated mental health professionals were recruited using various web-based methods. Participants were interviewed individually using the Zoom (Zoom Video Communications, Inc) videoconferencing platform. Interview questions focused on the potential benefits and risks of using conversational agents for health care, as well as the best way to integrate conversational agents into the health care system. Interviews were transcribed verbatim and uploaded to NVivo (version 12; QSR International, Inc) for thematic analysis.

RESULTS

A total of 24 health professionals participated in the study (19 women, 5 men; mean age 42.75, SD 10.71 years). Participants said that the use of conversational agents for health care could have certain benefits, such as greater access to care for patients or clients and workload support for health professionals. They also discussed potential drawbacks, such as an added burden on health professionals (eg, program familiarization) and the limited capabilities of these programs. Participants said that conversational agents could be used for routine or basic tasks, such as screening and assessment, providing information and education, and supporting individuals between appointments. They also said that health professionals should have some oversight in terms of the development and implementation of these programs.

CONCLUSIONS

The results of this study provide insight into health professionals' views on the use of conversational agents for health care, particularly in terms of the benefits and drawbacks of these programs and how they should be integrated into the health care system. These collective findings offer useful information and guidance to stakeholders who have an interest in the development and implementation of this technology.

A systematic review of chatbot-assisted interventions for substance use

Objectives

This study systematically reviewed research on the utilization of chatbot-related technologies for the prevention, assessment, and treatment of various substance uses, including alcohol, nicotine, and other drugs.

Methods

Following PRISMA guidelines, 28 articles were selected for final analysis from an initial screening of 998 references. Data were coded for multiple components, including study characteristics, intervention types, intervention contents, sample characteristics, substance use details, measurement tools, and main findings, particularly emphasizing the effectiveness of chatbot-assisted interventions on substance use and the facilitators and barriers affecting program effectiveness.

Results

Half of the studies specifically targeted smoking. Furthermore, over 85% of interventions were designed to treat substance use, with 7.14% focusing on prevention and 3.57% on assessment. Perceptions of effectiveness in quitting substance use varied, ranging from 25% to 50%, while for reduced substance use, percentages ranged from 66.67% to 83.33%. Among the studies assessing statistical effectiveness (46.43%), all experimental studies, including quasi-experiments, demonstrated significant and valid effects. Notably, 30% of studies emphasized personalization and providing relevant tips or information as key facilitators.

Conclusion

This study offers valuable insights into the development and validation of chatbot-assisted interventions, thereby establishing a robust foundation for their efficacy.

The conceptual potential of 'more-than-human care': A reflection with an artisanal fishing village in Brazil

As dominant approaches to biodiversity loss and climate change continue to fail in mitigating current socio-environmental crises, scholars and activists are exploring novel conceptual frameworks to drive transformative change in conservation. Among these, more-than-human care has emerged as a concept at the intersection of feminist debates about care and post-humanist discussions around the more-than-human. Although more-than-human care is increasingly referenced in the literature, it remains sparsely situated in the Global South. This article investigates the concept's potential to articulate care relations that can foster more plural, respectful, and transformative biodiversity conservation practices in local communities. Our research is situated in an artisanal fishing village in Brazil, where we engaged with both human and non-human members of the community to engage with their perspectives on more-than-human care and its relevance to their local concerns. From our analysis, four key dimensions emerged: the vital and everyday nature of caring; the relationality between humans and non-humans; reciprocity; and the fostering of flourishing for as many as possible. Our core argument is that the interaction between community practices and care theory enables new perspectives that center on daily and reciprocal care relations often overlooked in mainstream conservation approaches. By situating the conceptual potential of more-than-human care within the Global South, we underscore its ability to make the invisible visible and to inspire action for transformative change.

Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review

Introduction

Online mental healthcare has gained significant attention due to its effectiveness, accessibility, and scalability in the management of mental health symptoms. Despite these advantages over traditional in-person formats, including higher availability and accessibility, issues with low treatment adherence and high dropout rates persist. Artificial intelligence (AI) technologies could help address these issues, through powerful predictive models, language analysis, and intelligent dialogue with users, however the study of these applications remains underexplored. The following mixed methods review aimed to supplement this gap by synthesizing the available evidence on the applications of AI in online mental healthcare.

Method

We searched the following databases: MEDLINE, CINAHL, PsycINFO, EMBASE, and Cochrane. This review included peer-reviewed randomized controlled trials, observational studies, non-randomized experimental studies, and case studies that were selected using the PRISMA guidelines. Data regarding pre and post-intervention outcomes and AI applications were extracted and analyzed. A mixed-methods approach encompassing meta-analysis and network meta-analysis was used to analyze pre and post-intervention outcomes, including main effects, depression, anxiety, and study dropouts. We applied the Cochrane risk of bias tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) to assess the quality of the evidence.

Results

Twenty-nine studies were included revealing a variety of AI applications including triage, psychotherapy delivery, treatment monitoring, therapy engagement support, identification of effective therapy features, and prediction of treatment response, dropout, and adherence. AI-delivered self-guided interventions demonstrated medium to large effects on managing mental health symptoms, with dropout rates comparable to non-AI interventions. The quality of the data was low to very low.

Discussion

The review supported the use of AI in enhancing treatment response, adherence, and improvements in online mental healthcare. Nevertheless, given the low quality of the available evidence, this study highlighted the need for additional robust and high-powered studies in this emerging field.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=443575, identifier CRD42023443575.

Beliefs, experiences and concerns of using artificial intelligence in healthcare: A qualitative synthesis

Objective

Artificial intelligence (AI) is a developing field in the context of healthcare. As this technology continues to be implemented in patient care, there is a growing need to understand the thoughts and experiences of stakeholders in this area to ensure that future AI development and implementation is successful. The aim of this study was to conduct a literature search of qualitative studies exploring the opinions of stakeholders such as clinicians, patients, and technology experts in order to establish the most common themes and ideas that have been presented in this research.

Methods

A literature search was conducted of existing qualitative research on stakeholder beliefs about the use of AI use in healthcare. Twenty-one papers were selected and analysed resulting in the development of four key themes relating to patient care, patient-doctor relationships, lack of education and resources, and the need for regulations.

Results

Overall, patients and healthcare workers are open to the use of AI in care and appear positive about potential benefits. However, concerns were raised relating to the lack of empathy in interactions of AI tools, and potential risks that may arise from the data collection needed for AI use and development. Stakeholders in the healthcare, technology, and business sectors all stressed that there was a lack of appropriate education, funding, and guidelines surrounding AI, and these concerns needed to be addressed to ensure future implementation is safe and suitable for patient care.

Conclusion

Ultimately, the results found in this study highlighted that there was a need for communication between stakeholder in order for these concerns to be addressed, mitigate potential risks, and maximise benefits for patients and clinicians alike. The results also identified a need for further qualitative research in this area to further understand stakeholder experiences as AI use continues to develop.

Chatbots to Improve Sexual and Reproductive Health: Realist Synthesis

BACKGROUND

Digital technologies may improve sexual and reproductive health (SRH) across diverse settings. Chatbots are computer programs designed to simulate human conversation, and there is a growing interest in the potential for chatbots to provide responsive and accurate information, counseling, linkages to products and services, or a companion on an SRH journey.

OBJECTIVE

This review aimed to identify assumptions about the value of chatbots for SRH and collate the evidence to support them.

METHODS

We used a realist approach that starts with an initial program theory and generates causal explanations in the form of context, mechanism, and outcome configurations to test and develop that theory. We generated our program theory, drawing on the expertise of the research team, and then searched the literature to add depth and develop this theory with evidence.

RESULTS

The evidence supports our program theory, which suggests that chatbots are a promising intervention for SRH information and service delivery. This is because chatbots offer anonymous and nonjudgmental interactions that encourage disclosure of personal information, provide complex information in a responsive and conversational tone that increases understanding, link to SRH conversations within web-based and offline social networks, provide immediate support or service provision 24/7 by automating some tasks, and provide the potential to develop long-term relationships with users who return over time. However, chatbots may be less valuable where people find any conversation about SRH (even with a chatbot) stigmatizing, for those who lack confidential access to digital devices, where conversations do not feel natural, and where chatbots are developed as stand-alone interventions without reference to service contexts.

CONCLUSIONS

Chatbots in SRH could be developed further to automate simple tasks and support service delivery. They should prioritize achieving an authentic conversational tone, which could be developed to facilitate content sharing in social networks, should support long-term relationship building with their users, and should be integrated into wider service networks.

Staying with the silence: Silence as affording care in online alcohol and other drug counselling

As the name 'talk therapy' suggests, a key aim of alcohol and other drug counselling, psychotherapy and other talk therapies is to discuss issues, concerns and feelings with a health professional. Implicit here is the therapeutic value of talking through issues with a trained professional. But as with all interactions, therapeutic encounters involve silences and pauses as key aspects of the communicative process. Despite their ubiquity in the therapeutic encounter, research tends to either dismiss silences as inconsequential or as having undesirable effects, such as generating awkwardness or even disengagement from treatment. Drawing on Latour's (2002) concept of 'affordance' and a qualitative study of an Australian alcohol and other drug counselling service, we explore the varied functions of silences in online text-based counselling sessions. For clients, these include the role of silence in affording opportunities to engage in other everyday practices, such as socialising, caregiving or working - practices that can generate comfort and reduce distress, which in turn may support the therapeutic encounter. Similarly, for counsellors, temporal silences provide opportunities to confer with other counsellors and provide tailored care. However, protracted silences can raise concerns about the safety and wellbeing of clients who do not respond promptly or who exit encounters unexpectedly. Similarly, the sudden cessation of online care encounters (often associated with technical difficulties) can leave clients feeling frustrated and confused. In tracking these diverse affordances of silence, we draw attention to its generative potential in care encounters. We conclude by exploring the implications of our analysis for conceptions of care that underpin alcohol and other drug treatment.

Usability and Credibility of a COVID-19 Vaccine Chatbot for Young Adults and Health Workers in the United States: Formative Mixed Methods Study

BACKGROUND

The COVID-19 pandemic raised novel challenges in communicating reliable, continually changing health information to a broad and sometimes skeptical public, particularly around COVID-19 vaccines, which, despite being comprehensively studied, were the subject of viral misinformation. Chatbots are a promising technology to reach and engage populations during the pandemic. To inform and communicate effectively with users, chatbots must be highly usable and credible.

OBJECTIVE

We sought to understand how young adults and health workers in the United States assessed the usability and credibility of a web-based chatbot called Vira, created by the Johns Hopkins Bloomberg School of Public Health and IBM Research using natural language processing technology. Using a mixed method approach, we sought to rapidly improve Vira's user experience to support vaccine decision-making during the peak of the COVID-19 pandemic.

METHODS

We recruited racially and ethnically diverse young people and health workers, with both groups from urban areas of the United States. We used the validated Chatbot Usability Questionnaire to understand the tool's navigation, precision, and persona. We also conducted 11 interviews with health workers and young people to understand the user experience, whether they perceived the chatbot as confidential and trustworthy, and how they would use the chatbot. We coded and categorized emerging themes to understand the determining factors for participants' assessment of chatbot usability and credibility.

RESULTS

In all, 58 participants completed a web-based usability questionnaire and 11 completed in-depth interviews. Most questionnaire respondents said the chatbot was "easy to navigate" (51/58, 88%) and "very easy to use" (50/58, 86%), and many (45/58, 78%) said its responses were relevant. The mean Chatbot Usability Questionnaire score was 70.2 (SD 12.1) and scores ranged from 40.6 to 95.3. Interview participants felt the chatbot achieved high usability due to its strong functionality, performance, and perceived confidentiality and that the chatbot could attain high credibility with a redesign of its cartoonish visual persona. Young people said they would use the chatbot to discuss vaccination with hesitant friends or family members, whereas health workers used or anticipated using the chatbot to support community outreach, save time, and stay up to date.

CONCLUSIONS

This formative study conducted during the pandemic's peak provided user feedback for an iterative redesign of Vira. Using a mixed method approach provided multidimensional feedback, identifying how the chatbot worked well-being easy to use, answering questions appropriately, and using credible branding-while offering tangible steps to improve the product's visual design. Future studies should evaluate how chatbots support personal health decision-making, particularly in the context of a public health emergency, and whether such outreach tools can reduce staff burnout. Randomized studies should also be conducted to measure how chatbots countering health misinformation affect user knowledge, attitudes, and behavior.

'It's a different way to do medicine': Exploring the affordances of telehealth for hepatitis C healthcare

While the use of telehealth was common in hepatitis C healthcare in Australia in remote and regional areas prior to the COVID-19 pandemic, it has been used more broadly to improve access to hepatitis C heathcare during the pandemic. Despite its widespread uptake, little research has explored how telehealth shapes hepatitis C healthcare. In this article, we draw on the concept of affordances (Latour, 2002) and interviews with 25 healthcare practitioners to explore the emergent possibilities for hepatitis C care that take shape through telehealth. Despite suggestions that telehealth is comparable to in-person care, healthcare practitioners' accounts suggest that telehealth significantly changes the nature of their experience of providing healthcare for hepatitis C. According to these service practitioners, while it increased access to hepatitis C healthcare during the pandemic, it also afforded narrower, less personal healthcare encounters, with a focus on simple and singular issues, and reduced opportunities for communication and rapport. These affordances also discouraged the use of interpreters and by extension the inclusion of patients from non-English speaking backgrounds. However, the data collected also suggest that telehealth has the potential to afford more informal and relaxed healthcare environments and dispositions between healthcare practitioners and patients, potentially disrupting classic practitioner-patient power dynamics. In concluding, the article considers how telehealth models of care might better afford quality hepatitis C healthcare and care beyond COVID-19 pandemic conditions.

Exploring the potential of delivering mental health care services using digital technologies in Bangladesh: A qualitative analysis

BACKGROUND

Bangladesh is a lower-middle-income country affected by a severe lack of mental health service availability due to a scarcity of mental health experts, limited mental health literacy, and community stigma. In other low and middle-income countries, the online provision of mental health care services has addressed issues affecting service availability, accessibility, mass awareness of services, and stigma.

OBJECTIVE

The current study sought to understand stakeholders' perceptions of the potential of digital media-based mental health care delivery in strengthening Bangladesh's mental health system.

METHOD

Online in-depth interviews were conducted with seven psychiatrists and eleven people with lived experiences of mental health issues. In addition, two online focus groups were conducted with ten psychologists and nine mental health entrepreneurs. A thematic analysis of the audio transcriptions was used to identify themes.

RESULT

Stakeholders perceived that the benefits of digital media-based mental health services included the potential of increasing the awareness, availability, and accessibility of mental health services. Participants recommended: the rehabilitation of existing pathways; the use of social media to raise awareness; and the implementation of strategies that integrate different digital-based services to strengthen the mental health system and foster positive mental health-seeking behaviors.

CONCLUSION

Growing mental health awareness, combined with the appropriate use of digital media as a platform for distributing information and offering mental services, can help to promote mental health care. To strengthen mental health services in Bangladesh, tailored services, increased network coverage, and training are required on digital mental health.

Using AI chatbots to provide self-help depression interventions for university students: A randomized trial of effectiveness

BACKGROUND

Depression impacts the lives of a large number of university students. Mobile-based therapy chatbots are increasingly being used to help young adults who suffer from depression. However, previous trials have short follow-up periods. Evidence of effectiveness in pragmatic conditions are still in lack.

OBJECTIVE

This study aimed to compare chatbot therapy to bibliotherapy, which is a widely accepted and proven-useful self-help psychological intervention. The main objective of this study is to add to the evidence of effectiveness for chatbot therapy as a convenient, affordable, interactive self-help intervention for depression.

METHODS

An unblinded randomized controlled trial with 83 university students was conducted. The participants were randomly assigned to either a chatbot test group (n = 41) to receive a newly developed chatbot-delivered intervention, or a bibliotherapy control group (n = 42) to receive a minimal level of bibliotherapy. A set of questionnaires was implemented as measurements of clinical variables at baseline and every 4 weeks for a period of 16 weeks, which included the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder scale (GAD-7), the Positive and Negative Affect Scale (PANAS). The Client Satisfaction Questionnaire-8 (CSQ-8) and the Working Alliance Inventory-Short Revised (WAI-SR) were used to measure satisfaction and therapeutic alliance after the intervention. Participants' self-reported adherence and feedback on the therapy chatbot were also collected.

RESULTS

Participants were all university students (undergraduate students (n = 31), postgraduate students (n = 52)). They were between 19 and 28 years old (mean = 23.08, standard deviation (SD) = 1.76) and 55.42% (46/83) female. 24.07% (20/83) participants were lost to follow-up. No significant group difference was found at baseline. In the intention-to-treat analysis, individuals in the chatbot test group showed a significant reduction in the PHQ-9 scores (F = 22.89; P < 0.01) and the GAD-7 scores (F = 5.37; P = 0.02). Follow-up analysis of completers suggested that the reduction of anxiety was significant only in the first 4 weeks. The WAI-SR scores in the chatbot group were higher compared to the bibliotherapy group (t = 7.29; P < 0.01). User feedback showed that process factors were more influential than the content factors.

CONCLUSIONS

The chatbot-delivered self-help depression intervention was proven to be superior to the minimal level of bibliotherapy in terms of reduction on depression, anxiety, and therapeutic alliance achieved with participants.

Facilitators and Barriers to the Adoption of Telemedicine During the First Year of COVID-19: Systematic Review

BACKGROUND

The virulent and unpredictable nature of COVID-19 combined with a change in reimbursement mechanisms both forced and enabled the rapid adoption of telemedicine around the world. Thus, it is important to now assess the effects of this rapid adoption and to determine whether the barriers to such adoption are the same today as they were under prepandemic conditions.

OBJECTIVE

The objective of this systematic literature review was to examine the research literature published during the COVID-19 pandemic to identify facilitators, barriers, and associated medical outcomes as a result of adopting telemedicine, and to determine if changes have occurred in the industry during this time.

METHODS

The systematic review was performed in accordance with the Kruse protocol and the results are reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We analyzed 46 research articles from five continents published during the first year of the COVID-19 pandemic that were retrieved from searches in four research databases: PubMed (MEDLINE), CINAHL, Science Direct, and Web of Science.

RESULTS

Reviewers identified 25 facilitator themes and observations, 12 barrier themes and observations, and 14 results (compared to a control group) themes and observations. Overall, 22% of the articles analyzed reported strong satisfaction or satisfaction (zero reported a decline in satisfaction), 27% reported an improvement in administrative or efficiency results (as compared with a control group), 14% reported no statistically significant difference from the control group, and 40% and 10% reported an improvement or no statistically significant difference in medical outcomes using the telemedicine modality over the control group, respectively.

CONCLUSIONS

The pandemic encouraged rapid adoption of telemedicine, which also encouraged practices to adopt the modality regardless of the challenges identified in previous research. Several barriers remain for health policymakers to address; however, health care administrators can feel confident in the modality as the evidence largely shows that it is safe, effective, and widely accepted.

Futures-oriented drugs policy research: Events, trends, and speculating on what might become

One concern in the field of drugs policy is how to make research more futures-oriented. Tracing trends and events with the potential to alter drug futures are seen as ways of becoming more prepared. This challenge is made complex in fast evolving drug markets which entangle with shifting social and material relations at global scale. In this analysis, we argue that drugs policy research orientates to detection and discovery based on the recent past. This narrows future-oriented analyses to the predictable and probable, imagined as extensions of the immediate and local present. We call for a more speculative approach; one which extends beyond the proximal, and one which orientates to possibilities rather than probabilities. Drawing on ideas on speculation from science and technology and futures studies, we argue that speculative research holds potential for more radical alterations in drugs policy. We encourage research approaches which not only valorise knowing in relation to what might happen but which conduct experiments on what could be. Accordingly, we trace how speculative research makes a difference by altering the present through making deliberative interventions on alternative policy options, including policy scenarios which make a radical break with the present. We look specifically at the 'Big Event' and 'Mega Trend' as devices of speculative intervention in futures-oriented drugs policy research. We illustrate how the device of Mega Trend helps to trace as well as to speculate on some of the entangling elements affecting drug futures, including in relation to climate, environment, development, population, drug production, digitalisation, biotechnology, policy and discourse.