Chat Sessions Research Articles

Remote Exercise Engagement Among Individuals With Cardiovascular Disease

Purpose: To systematically review the literature regarding barriers and facilitators to remote exercise among individuals with cardiovascular diseases (CVD). Review Methods: Six databases were searched (inception—December 2023). Studies including barriers and facilitators for remote exercise reported by individuals living with CVD were considered. Quality was rated using the Mixed Methods Assessment Tool (MMAT). Results were synthesized following a thematic analysis approach. Characteristics of interventions were reported using the Template for Intervention Description and Replication (TIDieR). Summary: From an initial pool of 8543 records, 18 studies met the inclusion criteria. Among these, 13 were qualitative studies, with individual interviews being the most frequent data extraction method. Nine studies received 5/5 MMAT scores (ie, high quality). Twelve studies reported remote exercise interventions, mostly delivered within cardiac rehabilitation, by physiotherapists, nurses, and dietitians, predominantly via telephone calls or videoconferences, with only one study incorporating text messages. Five key themes influencing remote exercise interventions were identified: technology (encompassing access and literacy, self-monitoring/motivation, and security concerns), individual health considerations (health/personal aspects and coronavirus disease-2019), social factors (support from health care team and family/social support), environment (facilities), and logistical factors (time and displacement). Several themes served as both facilitators and barriers. Key insights for remote exercise interventions included mobile accessibility, interactive chat sessions, minimal click interactions, short sessions, personalized feedback, and flexible timing. Results emphasize the importance of promoting social interaction and support between participants and health care teams to mitigate barriers and enhance facilitators in remote exercise interventions for individuals with CVD.

Evaluating the Accuracy of Artificial Intelligence-Based Chatbots on Pediatric Dentistry Questions in the Korean National Dental Board Exam

This study aimed to assess the competency of artificial intelligence (AI) in pediatric dentistry and compare it with that of dentists. We used open-source data obtained from the Korea Health Personnel Licensing Examination Institute. A total of 32 item multiple-choice pediatric dentistry exam questions were included. Two AI-based chatbots (ChatGPT 3.5 and Gemini) were evaluated. Each chatbot received the same questions seven times in separate chat sessions initiated on April 25, 2024. The accuracy was assessed by measuring the percentage of correct answers, and consistency was evaluated using Cronbach’s alpha coefficient. Both ChatGPT 3.5 and Gemini demonstrated similar accuracy, with no significant differences observed between them. However, neither chatbot achieved the minimum passing score set by the Pediatric Dentistry National Examination. However, both chatbots exhibited acceptable consistency in their responses. Within the limits of this study, both AI-based chatbots did not sufficiently answer the pediatric dentistry exam questions. This finding suggests that pediatric dentists should be aware of the advantages and limitations of this new tool and effectively utilize it to promote patient health.

Assessing ChatGPT's Competency in Addressing Interdisciplinary Inquiries on Chatbot Uses in Sports Rehabilitation: Simulation Study.

ChatGPT showcases exceptional conversational capabilities and extensive cross-disciplinary knowledge. In addition, it can perform multiple roles in a single chat session. This unique multirole-playing feature positions ChatGPT as a promising tool for exploring interdisciplinary subjects. The aim of this study was to evaluate ChatGPT's competency in addressing interdisciplinary inquiries based on a case study exploring the opportunities and challenges of chatbot uses in sports rehabilitation. We developed a model termed PanelGPT to assess ChatGPT's competency in addressing interdisciplinary topics through simulated panel discussions. Taking chatbot uses in sports rehabilitation as an example of an interdisciplinary topic, we prompted ChatGPT through PanelGPT to role-play a physiotherapist, psychologist, nutritionist, artificial intelligence expert, and athlete in a simulated panel discussion. During the simulation, we posed questions to the panel while ChatGPT acted as both the panelists for responses and the moderator for steering the discussion. We performed the simulation using ChatGPT-4 and evaluated the responses by referring to the literature and our human expertise. By tackling questions related to chatbot uses in sports rehabilitation with respect to patient education, physiotherapy, physiology, nutrition, and ethical considerations, responses from the ChatGPT-simulated panel discussion reasonably pointed to various benefits such as 24/7 support, personalized advice, automated tracking, and reminders. ChatGPT also correctly emphasized the importance of patient education, and identified challenges such as limited interaction modes, inaccuracies in emotion-related advice, assurance of data privacy and security, transparency in data handling, and fairness in model training. It also stressed that chatbots are to assist as a copilot, not to replace human health care professionals in the rehabilitation process. ChatGPT exhibits strong competency in addressing interdisciplinary inquiry by simulating multiple experts from complementary backgrounds, with significant implications in assisting medical education.

Use of Generative AI to Identify Helmet Status Among Patients With Micromobility-Related Injuries From Unstructured Clinical Notes

Large language models (LLMs) have potential to increase the efficiency of information extraction from unstructured clinical notes in electronic medical records. To assess the utility and reliability of an LLM, ChatGPT-4 (OpenAI), to analyze clinical narratives and identify helmet use status of patients injured in micromobility-related accidents. This cross-sectional study used publicly available, deidentified 2019 to 2022 data from the US Consumer Product Safety Commission's National Electronic Injury Surveillance System, a nationally representative stratified probability sample of 96 hospitals in the US. Unweighted estimates of e-bike, bicycle, hoverboard, and powered scooter-related injuries that resulted in an emergency department visit were used. Statistical analysis was performed from November 2023 to April 2024. Patient helmet status (wearing vs not wearing vs unknown) was extracted from clinical narratives using (1) a text string search using researcher-generated text strings and (2) the LLM by prompting the system with low-, intermediate-, and high-detail prompts. The level of agreement between the 2 approaches across all 3 prompts was analyzed using Cohen κ test statistics. Fleiss κ was calculated to measure the test-retest reliability of the high-detail prompt across 5 new chat sessions and days. Performance statistics were calculated by comparing results from the high-detail prompt to classifications of helmet status generated by researchers reading the clinical notes (ie, a criterion standard review). Among 54 569 clinical notes, moderate (Cohen κ = 0.74 [95% CI, 0.73-0.75) and weak (Cohen κ = 0.53 [95% CI, 0.52-0.54]) agreement were found between the text string-search approach and the LLM for the low- and intermediate-detail prompts, respectively. The high-detail prompt had almost perfect agreement (κ = 1.00 [95% CI, 1.00-1.00]) but required the greatest amount of time to complete. The LLM did not perfectly replicate its analyses across new sessions and days (Fleiss κ = 0.91 across 5 trials; P < .001). The LLM often hallucinated and was consistent in replicating its hallucinations. It also showed high validity compared with the criterion standard (n = 400; κ = 0.98 [95% CI, 0.96-1.00]). This study's findings suggest that although there are efficiency gains for using the LLM to extract information from clinical notes, the inadequate reliability compared with a text string-search approach, hallucinations, and inconsistent performance significantly hinder the potential of the currently available LLM.

Automated Behavioral Coding to Enhance the Effectiveness of Motivational Interviewing in a Chat-Based Suicide Prevention Helpline: Secondary Analysis of a Clinical Trial.

With the rise of computer science and artificial intelligence, analyzing large data sets promises enormous potential in gaining insights for developing and improving evidence-based health interventions. One such intervention is the counseling strategy motivational interviewing (MI), which has been found effective in improving a wide range of health-related behaviors. Despite the simplicity of its principles, MI can be a challenging skill to learn and requires expertise to apply effectively. This study aims to investigate the performance of artificial intelligence models in classifying MI behavior and explore the feasibility of using these models in online helplines for mental health as an automated support tool for counselors in clinical practice. We used a coded data set of 253 MI counseling chat sessions from the 113 Suicide Prevention helpline. With 23,982 messages coded with the MI Sequential Code for Observing Process Exchanges codebook, we trained and evaluated 4 machine learning models and 1 deep learning model to classify client- and counselor MI behavior based on language use. The deep learning model BERTje outperformed all machine learning models, accurately predicting counselor behavior (accuracy=0.72, area under the curve [AUC]=0.95, Cohen κ=0.69). It differentiated MI congruent and incongruent counselor behavior (AUC=0.92, κ=0.65) and evocative and nonevocative language (AUC=0.92, κ=0.66). For client behavior, the model achieved an accuracy of 0.70 (AUC=0.89, κ=0.55). The model's interpretable predictions discerned client change talk and sustain talk, counselor affirmations, and reflection types, facilitating valuable counselor feedback. The results of this study demonstrate that artificial intelligence techniques can accurately classify MI behavior, indicating their potential as a valuable tool for enhancing MI proficiency in online helplines for mental health. Provided that the data set size is sufficiently large with enough training samples for each behavioral code, these methods can be trained and applied to other domains and languages, offering a scalable and cost-effective way to evaluate MI adherence, accelerate behavioral coding, and provide therapists with personalized, quick, and objective feedback.

Theory of mind performance of large language models: A comparative analysis of Turkish and English

Theory of mind (ToM), understanding others’ mental states, is a defining skill belonging to humans. Research assessing LLMs’ ToM performance yields conflicting findings and leads to discussions about whether and how they could show ToM understanding. Psychological research indicates that the characteristics of a specific language can influence how mental states are represented and communicated. Thus, it is reasonable to expect language characteristics to influence how LLMs communicate with humans, especially when the conversation involves references to mental states. This study examines how these characteristics affect LLMs’ ToM performance by evaluating GPT 3.5 and 4 performances in English and Turkish. Turkish provides an excellent contrast to English since Turkish has a different syntactic structure and special verbs, san- and zannet-, meaning “falsely believe.” Using Open AI's Chat Completion API, we collected responses from GPT models for first- and second-order ToM scenarios in English and Turkish. Our innovative approach combined completion prompts and open-ended questions within the same chat session, offering deep insights into models’ reasoning processes. Our data showed that while GPT models can respond accurately to standard ToM tasks (100% accuracy), their performance deteriorates (below chance level) with slight modifications. This high sensitivity suggests a lack of robustness in ToM performance. GPT 4 outperformed its predecessor, GPT 3.5, showing improvement in ToM performance to some extent. The models generally performed better when tasks were presented in English than in Turkish. These findings indicate that GPT models cannot reliably pass first-order and second-order ToM tasks in either of the languages yet. The findings have significant implications for Explainability of LLMs by highlighting challenges and biases that they face when simulating human-like ToM understanding in different languages.

Evaluating the performance of ChatGPT-3.5 and ChatGPT-4 on the Taiwan plastic surgery board examination

BackgroundChat Generative Pre-Trained Transformer (ChatGPT) is a state-of-the-art large language model that has been evaluated across various medical fields, with mixed performance on licensing examinations. This study aimed to assess the performance of ChatGPT-3.5 and ChatGPT-4 in answering questions from the Taiwan Plastic Surgery Board Examination. MethodsThe study evaluated the performance of ChatGPT-3.5 and ChatGPT-4 on 1375 questions from the past 8 years of the Taiwan Plastic Surgery Board Examination, including 985 single-choice and 390 multiple-choice questions. We obtained the responses between June and July 2023, launching a new chat session for each question to eliminate memory retention bias. ResultsOverall, ChatGPT-4 outperformed ChatGPT-3.5, achieving a 59 % correct answer rate compared to 41 % for ChatGPT-3.5. ChatGPT-4 passed five out of eight yearly exams, whereas ChatGPT-3.5 failed all. On single-choice questions, ChatGPT-4 scored 66 % correct, compared to 48 % for ChatGPT-3.5. On multiple-choice, ChatGPT-4 achieved a 43 % correct rate, nearly double the 23 % of ChatGPT-3.5. ConclusionAs ChatGPT evolves, its performance on the Taiwan Plastic Surgery Board Examination is expected to improve further. The study suggests potential reforms, such as incorporating more problem-based scenarios, leveraging ChatGPT to refine exam questions, and integrating AI-assisted learning into candidate preparation. These advancements could enhance the assessment of candidates' critical thinking and problem-solving abilities in the field of plastic surgery.

CHAT MARKETING'S IMPACT ON CUSTOMER SUPPORT SATISFACTION IN FINANCIAL STARTUPS

The subject of this paper is the influence of chat marketing on customer support satisfaction. The purpose of the study is to determine the relationship between the use of chat marketing and customer support satisfaction, as well as to identify the essential factors that contribute to this relationship. Furthermore, this paper aims to provide theoretical insights into the influence of chat marketing on customer support satisfaction in financial startups. Many companies struggle to achieve the most efficient chat session and are uncertain about the best strategies, factors and types of chat to implement. Therefore, these findings will help financial startups to theoretically understand the aspects of chat marketing and customer support satisfaction. The object of this article is the impact of chat marketing on customer satisfaction, and the purpose is to reveal theoretical knowledge that will help identify the factors that contribute to chat marketing and customer satisfaction in financial startups. This paper discusses the importance of customer support for financial startups and the different approaches to providing customer support through emotional means. In addition, this paper highlights the importance of customer satisfaction through trust and commitment to build a positive relationship with the customer. Furthermore, this paper demonstrated the framework behind chat marketing, which includes the use of different tools such as live chat platforms, chatbots and live agents or human representatives that help to achieve customer satisfaction through customer support. Moreover, using methods such as: observation, survey and regression analysis, this paper has demonstrated the importance of customer centricity in the financial sector to provide key essentials for employees as incentives that maintain customer satisfaction, while ensuring the flexibility of real-time communication with chatbots or live agents, with the need to have the ease of use and flexibility and various other factors to contribute to customer support satisfaction. As a result, the paper emphasises that in order to increase customer satisfaction with financial start-ups, it is crucial to maintain a low response rate by providing fast and reliable customer assistance. Developing and regularly updating chatbots to facilitate smooth transitions to live agents in complex situations is essential. Implementing an effective ticketing system to automate and monitor issue resolution is essential, with a focus on resolving issues during chat interactions whenever possible. Adopting a hybrid strategy that combines live agents and chatbots helps to meet diverse customer needs in a timely manner. Investing in comprehensive training for live agents is necessary to ensure professional and timely service. In addition, continuous monitoring and analysis of customer feedback plays a key role in improving overall business performance.

Structure and Engagement in Teacher–Student Chats in ESL Writing

Teacher-moderated online chats are a common option for conducting writing conferences with students. The efficacy of chat sessions is impacted by the expectations for and structure of these sessions. This corpus-based study used a move analysis to determine the purposes and organization of 129 chats between 3 experienced teachers and 106 students of primarily Chinese and Arabic origin in 9 freshman English as a second language (ESL) writing courses. Chats occurred after a draft of an essay assignment had been returned with written teacher feedback, in order to provide opportunities for students to receive individualized input. Findings show that chats are structured to serve three functions: opening and closing social moves (greetings, conversational pleasantries) meant to establish rapport; management moves that serve to structure and orient the chat; and instructional moves in which the teachers and students confer and additional feedback is provided. However, chats were not as effective at engaging participants as they could be due to a lack of intentionality and reciprocity. With prior research showing chats are a useful addition to other forms of teacher electronic feedback, we offer suggestions for improving the use of instructional chats in freshman ESL writing classes, in order to better engage students.

Effect of Adding Personalized Instant Messaging Apps to a Brief Smoking Cessation Model in Community Smokers in Hong Kong: Pragmatic Randomized Clinical Trial.

While text messaging has proven effective for smoking cessation (SC), engagement in the intervention remains suboptimal. This study aims to evaluate whether using more interactive and adaptive instant messaging (IM) apps on smartphones, which enable personalization and chatting with SC advisors, can enhance SC outcomes beyond the provision of brief SC advice and active referral (AR) to SC services. From December 2018 to November 2019, we proactively recruited 700 adult Chinese daily cigarette users in Hong Kong. Participants were randomized in a 1:1 ratio. At baseline, all participants received face-to-face brief advice on SC. Additionally, they were introduced to local SC services and assisted in selecting one. The intervention group received an additional 26 personalized regular messages and access to interactive chatting through IM apps for 3 months. The regular messages aimed to enhance self-efficacy, social support, and behavioral capacity for quitting, as well as to clarify outcome expectations related to cessation. We developed 3 sets of messages tailored to the planned quit date (within 30 days, 60 days, and undecided). Participants in the intervention group could initiate chatting with SC advisors on IM themselves or through prompts from regular messages or proactive inquiries from SC advisors. The control group received 26 SMS text messages focusing on general health. The primary outcomes were smoking abstinence validated by carbon monoxide levels of <4 parts per million at 6 and 12 months after the start of the intervention. Of the participants, 505/700 (72.1%) were male, and 450/648 (69.4%) were aged 40 or above. Planning to quit within 30 days was reported by 500/648 (77.2%) participants, with fewer intervention group members (124/332, 37.3%) reporting previous quit attempts compared with the control group (152/335, 45.4%; P=.04). At the 6- and 12-month follow-ups (with retention rates of 456/700, 65.1%, and 446/700, 63.7%, respectively), validated abstinence rates were comparable between the intervention (14/350, 4.0%, and 19/350, 5.4%) and control (11/350, 3.1% and 21/350, 6.0%) groups. Compared with the control group, the intervention group reported greater utilization of SC services at 12 months (RR 1.26, 95% CI 1.01-1.56). Within the intervention group, engaging in chat sessions with SC advisors predicted better validated abstinence at 6 months (RR 3.29, 95% CI 1.13-9.63) and any use of SC services (RR 1.66, 95% CI 1.14-2.43 at 6 months; RR 1.67, 95% CI 1.26-2.23 at 12 months). An IM-based intervention, providing support and assistance alongside brief SC advice and AR, did not yield further increases in quitting rates but did encourage the utilization of SC services. Future research could explore whether enhanced SC service utilization leads to improved long-term SC outcomes. ClinicalTrials.gov NCT03800719; https://clinicaltrials.gov/ct2/show/NCT03800719.

Look at Grandma! Joint visual attention over video chat during the COVID-19 pandemic

Social interactions are crucial for many aspects of development. One developmentally important milestone is joint visual attention (JVA), or shared attention between child and adult on an object, person, or event. Adults support infants’ development of JVA by structuring the input they receive, with the goal of infants learning to use JVA to communicate. When family members are separated from the infants in their lives, video chat sessions between children and distant relatives allow for shared back-and-forth turn taking interaction across the screen, but JVA is complicated by screen mediation. During video chat, when a participant is looking or pointing at the screen to something in the other person’s environment, there is no line of sight that can be followed to their object of focus. Sensitive caregivers in the remote and local environment with the infant may be able to structure interactions to support infants in using JVA to communicate across screens. We observed naturalistic video chat interactions longitudinally from 50 triads (infant, co-viewing parent, remote grandmother). Longitudinal growth models showed that JVA rate changes with child age (4 to 20 months). Furthermore, grandmother sensitivity predicted JVA rate and infant attention. More complex sessions (sessions involving more people, those with a greater proportion of across-screen JVA, and those where infants initiated more of the JVA) resulted in lower amounts of JVA-per-minute, and evidence of family-level individual differences emerged in all models. We discuss the potential of video chat to enhance communication for separated families in the digital world.

Predicting suicide risk in real-time crisis hotline chats integrating machine learning with psychological factors: Exploring the black box.

This study addresses the suicide risk predicting challenge by exploring the predictive ability of machine learning (ML) models integrated with theory-driven psychological risk factors in real-time crisis hotline chats. More importantly, we aimed to understand the specific theory-driven factors contributing to the ML prediction of suicide risk. The dataset consisted of 17,654 crisis hotline chat sessions classified dichotomously as suicidal or not. We created a suicide risk factors-based lexicon (SRF), which encompasses language representations of key risk factors derived from the main suicide theories. The ML model (Suicide Risk-Bert; SR-BERT) was trained using natural language processing techniques incorporating the SRF lexicon. The results showed that SR-BERT outperformed the other models. Logistic regression analysis identified several theory-driven risk factors significantly associated with suicide risk, the prominent ones were hopelessness, history of suicide, self-harm, and thwarted belongingness. The lexicon is limited in its ability to fully encompass all theoretical concepts related to suicide risk, nor to all the language expressions of each concept. The classification of chats was determined by trained but non-professionals in metal health. This study highlights the potential of how ML models combined with theory-driven knowledge can improve suicide risk prediction. Our study underscores the importance of hopelessness and thwarted belongingness in suicide risk and thus their role in suicide prevention and intervention.

Virtual Teaching Together: engaging parents and young children in STEM activities.

Early informal learning experiences are essential for sparking long-term interest in science, technology, engineering, and math (STEM). In a prior study, we found more promising parent involvement outcomes when families of young children were provided with STEM family education events along with home STEM activity kits compared to providing workshops alone. This study was a conceptual replication using the same program-Teaching Together STEM-to deliver educational workshops plus home activity kits; however, we varied the delivery method by using virtual "funshops" to evaluate if parents perceived this modality as feasible and useful. Museum informal science educators introduced four units via virtual video chat sessions linked to 12 hands-on STEM activities that were mailed to families randomly assigned to the treatment group. Half of the families were assigned to a waitlist control group that received a portion of the virtual program after the posttest. Participants included 60 families with children aged 3 to 5 years from diverse linguistic and socioeconomic backgrounds. Our results indicate no significant group differences in the primary outcome of parents' involvement in informal STEM but a small, positive effect size (ES = 0.18) that was similar in magnitude to the prior, in-person study. Although parents mostly perceived the remote delivery as convenient and the materials as engaging for their child, there were no significant program impacts on children's general science interests (ES = -0.19). Despite the convenience, parents reported time was a barrier to doing STEM activities at home. Parents with lower education levels were less likely to attend, suggesting virtual approaches are not sufficient for ensuring broad access to family engagement programs for populations underrepresented in STEM.

Comparing Artificial Intelligence and Senior Residents in Oral Lesion Diagnosis: A Comparative Study.

Artificial intelligence (AI) is a field of computer science that seeks to build intelligent machines that can carry out tasks that usually necessitate human intelligence. AI may help dentists with a variety of dental tasks, including clinical diagnosis and treatment planning. This study aims to compare the performance of AI and oral medicine residents in diagnosing different cases, providing treatment, and determining if it is reliable to assist them in their field of work. The study conducted a comparative analysis of the responses from third- and fourth-year residents trained in Oral Medicine and Pathology at King Saud University, College of Dentistry. The residents were given a closed multiple-choice test consisting of 19 questions with four response options labeled A-D and one question with five response options labeled A-E. The test was administered via Google Forms, and each resident's response was stored electronically in an Excel sheet (Microsoft® Corp., Redmond, WA). The residents' answers were then compared to the responses generated by three major language models: OpenAI, Stablediffusion, and PopAI. The questions were inputted into the language models in the same format as the original test, and prior to each question, an artificial intelligence chat session was created to eliminate memory retention bias. The input was done on November 19, 2023, the same day the official multiple-choice test was administered. The study had a sample size of 20 residents trained in Oral Medicine and Pathology at King Saud University, College of Dentistry, consisting of both third-year and fourth-year residents. The responses of three large language models (LLM), including OpenAI, Stablediffusion, and PopAI, as well as the responses of 20 senior residents for 20 clinical cases about oral lesion diagnosis. There were no significant variations observed for the remaining questions in the responses to only two questions (10%). For the remaining questions, there were no significant differences. The median (IQR) score of LLMs was 50.0 (45.0 to 60.0), with a minimum of 40 (for stable diffusion) and a maximum of 70 (for OpenAI). The median (IQR) score of senior residents was 65.0 (55.0-75.0). The highest and lowest scores of residents were 40 and 90, respectively. There was no significant difference in the percent scores of residents and LLMs (p = 0.211). The agreement level was measured using the Kappa value. The agreement among senior dental residents was observed to be weak, with a Kappa value of 0.396. In contrast, the agreement among LLMs demonstrated a moderate level, with a Kappa value of 0.622, suggesting a more cohesive alignment in responses among the artificial intelligence models. When comparing residents' responses with those generated by different OpenAI models, including OpenAI, Stablediffusion, and PopAI, the agreement levels were consistently categorized as weak, with Kappa values of 0.402, 0.381, and 0.392, respectively. What the current study reveals is that when comparing the response score, there is no significant difference, in contrast to the agreement analysis among the residents, which was low compared to the LLMs, in which it was high. Dentists should consider that AI is very beneficial in providing diagnosis and treatment and use it to assist them.

An internet-based behavioral intervention for adults with autism spectrum disorder – A randomized controlled trial and feasibility study

BackgroundThe increased prevalence of Autism Spectrum Disorder (ASD) diagnoses in combination with psychiatric comorbidity, has led to an increased need for effective interventions. The evidence for internet-based interventions for several mental health problems is established but has not been evaluated for adults with ASD. ObjectiveThe aim of this randomized controlled trial is to evaluate the feasibility and effects of an internet-based intervention targeting quality of life and psychiatric symptoms (depression and anxiety) in adults with ASD. Methods84 participants were randomly allocated to intervention (n = 42) or control (n = 42). The 18-week internet-based intervention covered a range of themes related to difficulties common in ASD, and exercises based on cognitive behavioral strategies. Participants were provided with individual feedback following each module and were invited to regular chat sessions with peer participants. The primary outcomes were subjective quality of life and sense of coherence, and secondary outcomes were symptoms of depression and anxiety. All outcomes were measured at five occasions and analysed with linear mixed effect models. Participant satisfaction and adherence was also analysed. ResultsParticipant satisfaction and adherence was satisfactory but no significant interaction between group and time was found for any outcome measure. Autistic traits were negatively related to quality of life and sense of coherence and positively related to anxiety and depressive symptoms. ConclusionsThis internet-based intervention showed feasibility regarding adherence and participant satisfaction. However, no significant effects on quality of life, sense of coherence or psychiatric symptoms were found, likely due to limitations in the design and methodology of this specific trial in combination to the heterogeneity of the group. Individuals with ASD may require interventions that are flexible and individually tailored in regard to both format, content and therapeutic support. The current trial provides useful information and suggestions for the future research on internet-based interventions for ASD.

Barriers to Seeking Medical Care Among Youth Victims of Sexual Violence

PurposeYouth victims of sexual violence often experience physical health problems but are unlikely to receive medical care. However, victims' reasons for not accessing medical care have been understudied. We examined barriers to seeking medical care among youth victims who contacted the National Sexual Assault Online Hotline. MethodsWe used archival data about one-on-one chat sessions with youth victims between June 2018 and February 2020. Hotline staff described victims' reasons for not accessing medical care via an online assessment. We coded and qualitatively examined these reasons using data about 520 victims with physical health concerns who had not received medical care. ResultsVictims' barriers were rooted in individual beliefs and contextual realities reflected in three categories: (1) perception that medical care was not needed, (2) anticipated consequences of seeking medical care, and (3) inability to physically access medical care. Victims who perceived care as unnecessary did not understand the health implications of abuse or minimized their need for care. Anticipated consequences included privacy and control over disclosure, stigmatization, retaliation from the perpetrator, family disruptions, and retraumatizing medical treatment. Victims unable to physically access care were uncertain how to access care independently, lacked social support, or were prevented from care by the perpetrator. DiscussionMedical treatment can ameliorate acute health concerns and increase safety, but youth victims perceived numerous barriers to care. Several barriers implied unintended consequences of child maltreatment policies, including mandatory reporting. Trauma-informed policy and practice are critical for improving victims' access to medical care and other support services.

Visualizing Synchronous Virtual Reference Service in Chandigarh Region Innovation and Knowledge Cluster (CRIKC) Institutions in India: Librarians’ Perception

The present study aims to highlight the librarians’ perception pertaining to the implementation of Virtual Reference Service (VRS) in the Chandigarh Region Innovation and Knowledge Cluster Institutions (CRIKC). The study is based on a sample drawn from librarians of the CRIKC libraries. To assess and evaluate the librarians’ perception regarding the feasibility of VRS, a questionnaire was prepared and distributed among 19 library professionals of CRIKC libraries. The reason for choosing the questionnaire was to know the status of virtual reference service as well as librarians’ perception regarding the feasibility of VRS in CRIKC libraries. The findings suggest that the major challenge faced by librarians in providing VRS is the need for an additional budget for staffing, followed by an increased staff workload and a lack of staff training facilities. However, despite these challenges, the librarians perceive VRS to be highly effective in promoting the optimum use of the library’s collection and resources, improving staff efficiency, and promoting information literacy. From the users’ perspective, remote access to online assistance and faster access to information were found to be the most effective features of VRS. Additionally, personalized service, 24/7 availability, and the ability to save chat sessions for later use were also perceived as beneficial by most of the librarians. These results highlight the potential benefits of implementing web 2.0-enabled VRS in academic libraries, despite the challenges involved. It is essential to allocate a sufficient budget for staffing and provide staff training facilities to overcome the challenges faced in implementing VRS successfully. Furthermore, by improving user satisfaction and promoting information literacy, VRS can prove to be an effective tool for enhancing library services and resources.

Enhancing questioning skills through child avatar chatbot training with feedback.

Training child investigative interviewing skills is a specialized task. Those being trained need opportunities to practice their skills in realistic settings and receive immediate feedback. A key step in ensuring the availability of such opportunities is to develop a dynamic, conversational avatar, using artificial intelligence (AI) technology that can provide implicit and explicit feedback to trainees. In the iterative process, use of a chatbot avatar to test the language and conversation model is crucial. The model is fine-tuned with interview data and realistic scenarios. This study used a pre-post training design to assess the learning effects on questioning skills across four child interview sessions that involved training with a child avatar chatbot fine-tuned with interview data and realistic scenarios. Thirty university students from the areas of child welfare, social work, and psychology were divided into two groups; one group received direct feedback (n = 12), whereas the other received no feedback (n = 18). An automatic coding function in the language model identified the question types. Information on question types was provided as feedback in the direct feedback group only. The scenario included a 6-year-old girl being interviewed about alleged physical abuse. After the first interview session (baseline), all participants watched a video lecture on memory, witness psychology, and questioning before they conducted two additional interview sessions and completed a post-experience survey. One week later, they conducted a fourth interview and completed another post-experience survey. All chatbot transcripts were coded for interview quality. The language model's automatic feedback function was found to be highly reliable in classifying question types, reflecting the substantial agreement among the raters [Cohen's kappa (κ) = 0.80] in coding open-ended, cued recall, and closed questions. Participants who received direct feedback showed a significantly higher improvement in open-ended questioning than those in the non-feedback group, with a significant increase in the number of open-ended questions used between the baseline and each of the other three chat sessions. This study demonstrates that child avatar chatbot training improves interview quality with regard to recommended questioning, especially when combined with direct feedback on questioning.

The Usefulness of a Smartphone App-Based Smoking Cessation Program for Conventional Cigarette Users, Heated Tobacco Product Users, and Dual Users: Retrospective Study.

Heated tobacco products (HTPs) are widespread in Japan, and smoking cessation of such products has become an important issue owing to the spread of harmful effects from HTPs. The efficacy of online digital therapy has been reported in smoking cessation treatment; however, we have limited evidence of online smoking cessation programs for HTP users. In this study, we evaluate the usefulness of the Ascure program for HTP users (defined as exclusive HTP use or dual use of HTP and cigarettes) compared with exclusive cigarette users. This was a retrospective study. We recruited adult smokers participating in the Ascure online smoking cessation program in Japan from June 2019 to February 2021. The Ascure smartphone app provided four elements: (1) educational video tutorials to enhance the understanding of nicotine dependence, (2) a personalized to-do list for behavior change, (3) a digital diary for record keeping, and (4) interactive chat sessions for relief from cravings or withdrawal symptoms. The primary outcome was the continuous abstinence rate (CAR) at weeks 21 to 24, biochemically validated using salivary cotinine testing. We considered those who dropped out of the program as smoking cessation failures. We analyzed the primary outcome using inverse probability weighting against tobacco product type estimated by multinomial propensity scores. We also assessed CAR at weeks 9 to 12 and program adherence. We analyzed data from 2952 participants, including 52% (1524/3478) in the cigarette group, 35% (1038/3478) in the HTP group, and 13% (390/3478) in the dual-use group, who had a mean age of 43.4 (SD 10.8) years and included 17% (513/2952) women. CAR at weeks 21 to 24 showed that exclusive HTP users were more likely to stop tobacco use than exclusive cigarette smokers (CAR 52.6% for cigarette users vs CAR 64.8% for HTP users; odds ratio [OR] 1.17, 95% CI 1.12-1.22; P<.001). There was no significant difference between the exclusive cigarette users and the dual users (CAR 52.6% for cigarette users vs CAR 48.7% for dual users; OR 0.99, 95% CI 0.93-1.05; P=.77). CAR at weeks 9 to 12 was 56.7% (95% CI 54.2%-59.2%) for the exclusive cigarette users, 68.3% (95% CI 65.5%-71.1%) for the exclusive HTP users, and 58.2% (95% CI 53.3%-63.1%) for the dual users. The program adherence rate at week 24 was 70.7% overall (68.4% for cigarette users, 75% for HTP users, and 67.9% for dual users). Exclusive HTP users had higher CARs and adherence compared with exclusive cigarette users, indicating a higher affinity for the Ascure online smoking cessation program. This program might be a useful smoking cessation option for HTP users, as well as for cigarette smokers.

Práticas ocupacionais reivindicativas de ativistas com deficiência

Abstract Introduction The occupational praxis of activists with disabilities in the Latin America has presented actions of revindication from historically marginalised territories. Objective To explore and describe strategies used by Chilean activist with disabilities. Method Qualitative design via three research techniques: a) 11 in-depth interviews; b) six group chat sessions; c) content analysis of eight social networks belonging to collectives of activists with disabilities in Chile. Results Activists indicate various occupations for revindication as subjects with rights. These trajectories are exemplified with the following dimensions: 1) Interpellate full social participation: demanding justice and citizenship; 2) Showing defective bodies: public mobilisations; 3) Occupying institutional space by placing: bodies in the system. Conclusion Dissident occupational practices intervene and transform the limited comprehension about what human vulnerability and fragility is capable of. This situation is mainly appreciated in the Global South.