Text Feedback Research Articles

Study to assess existing knowledge of headache disorders among registered osteopaths practising in the UK: A cross-sectional survey

BackgroundHeadache disorders are highly prevalent worldwide. Many headache sufferers search for answers outside medical and pharmaceutical models. Complementary and alternative medicine (CAM) including osteopathy are widely used by headache patients. Indeed 9% of patients consulting osteopaths do so for headaches. There is no existing assessment of headache knowledge among practising Osteopaths in the UK. MethodsThis study was a cross-sectional survey employing an anonymous online questionnaire. An original questionnaire was developed. The scope was based on established guidelines in the primary care setting. Multiple choice and closed-ended questions assessed knowledge of the diagnostic criteria, potential red flags and appropriate investigations. The answers were informed by the International Classification Headache Disorder (ICHD-3) and the British Association for the Study of Headaches (BASH) guidelines. Three case vignettes relevant to osteopathic practice were included. Participants were invited to give optional free text feedback. The questionnaire was open to be self-completed by participants for 30 days. ResultsThere were 398 responders of which 383 were included and all of whom completed the questionnaire. The study found areas where knowledge was good, areas where it was limited and areas of uncertainty. The mean knowledge score was 6.93 out of 10 (range 2.18–9.42). There was a significantly lower mean knowledge score in those who had no prior headache education of −0.716 with a difference in means (−1.075 to −0.353, 95% C.I.) compared to those with prior education. Gender and years qualified had no significant effect on mean knowledge score. Optional free text feedback was given by 81 (21.2%) of the respondents and analyzed. The main themes were acknowledgment of knowledge gaps and a desire for more headache education. ConclusionThe findings suggest there are gaps in the existing knowledge on headache among UK osteopaths and the extent of this is dependent on prior headache education. We propose that ongoing headache education among osteopaths is needed in the field.

Exculpation and Stigma in Tourette Syndrome

PurposeThere is a widespread recognition that biomedical explanations offer benefits to those diagnosed with a mental disorder. Recent research points out that such explanations may nevertheless have stigmatizing effects. In this study, this ‘mixed blessing’ [2] account of biomedical explanations is investigated in a case of philosophical interest: Tourette Syndrome.MethodWe conducted a vignette survey with 221 participants in which we first assessed quantitative attributions of blame as well as the desire for social distance for behavior associated with Tourette Syndrome.ResultsIn our study, it is confirmed that in the case of biomedical explanations, less blame is attributed than in the case of psychosocial explanations. When presented with a mixed (or an epigenetic) explanation stressing an entanglement of biological and psychosocial factors this did not increase blame attribution. The desire for social distance is unaffected by the type of explanation but the participants’ free text feedback indicates this might obfuscate an underlying dilemma between stigma and blame revealed in recent research.ConclusionThere seems to be potential for blame reduction in explanations where biological and psychosocial factors are entangled. However, dynamic, ‘epigenetic’, explanations require further qualitative research to be performed as well as a philosophical framework to account for the ‘mixed blessings’ account.

Leveraging AI and Machine Learning for National Student Survey: Actionable Insights from Textual Feedback to Enhance Quality of Teaching and Learning in UK’s Higher Education

Students’ evaluation of teaching, for instance, through feedback surveys, constitutes an integral mechanism for quality assurance and enhancement of teaching and learning in higher education. These surveys usually comprise both the Likert scale and free-text responses. Since the discrete Likert scale responses are easy to analyze, they feature more prominently in survey analyses. However, the free-text responses often contain richer, detailed, and nuanced information with actionable insights. Mining these insights is more challenging, as it requires a higher degree of processing by human experts, making the process time-consuming and resource intensive. Consequently, the free-text analyses are often restricted in scale, scope, and impact. To address these issues, we propose a novel automated analysis framework for extracting actionable information from free-text responses to open-ended questions in student feedback questionnaires. By leveraging state-of-the-art supervised machine learning techniques and unsupervised clustering methods, we implemented our framework as a case study to analyze a large-scale dataset of 4400 open-ended responses to the National Student Survey (NSS) at a UK university. These analyses then led to the identification, design, implementation, and evaluation of a series of teaching and learning interventions over a two-year period. The highly encouraging results demonstrate our approach’s validity and broad (national and international) application potential—covering tertiary education, commercial training, and apprenticeship programs, etc., where textual feedback is collected to enhance the quality of teaching and learning.

A Novel Online Teaching Effect Evaluation Model Based on Visual Question Answering

<p>The paper proposes a novel visual question answering (VQA)-based online teaching effect evaluation model. Based on the text interaction between teacher and students, we give a guide-attention (GA) model to discover the directive clues. Combining the self-attention (SA) models, we reweight the vital feature to locate the critical information on the whiteboard and students’ faces and further recognize their content and facial expressions. Three branches of information are encoded into the feature vectors to be fed into a bidirectional GRU network. With the real labels of the students’ answers annotated by two teachers and the predicted labels from the text and facial expression feedback, we train the chained network. Experiment reports a couple of competitive performance in the 2-class and 5-class tasks on the self-collected dataset, respectively.</p> <p> </p>

Detection of Touchscreen-Based Urdu Braille Characters Using Machine Learning Techniques

Revolution in technology is changing the way visually impaired people read and write Braille easily. Learning Braille in its native language can be more convenient for its users. This study proposes an improved backend processing algorithm for an earlier developed touchscreen-based Braille text entry application. This application is used to collect Urdu Braille data, which is then converted to Urdu text. Braille to text conversion has been done on Hindi, Arabic, Bangla, Chinese, English, and other languages. For this study, Urdu Braille Grade 1 data were collected with multiclass (39 characters of Urdu represented by class 1, Alif (ﺍ), to class 39, Bri Yay (ے). Total (N = 144) cases for each class were collected. The dataset was collected from visually impaired students from The National Special Education School. Visually impaired users entered the Urdu Braille alphabets using touchscreen devices. The final dataset contained (N = 5638) cases. Reconstruction Independent Component Analysis (RICA)-based feature extraction model is created for Braille to Urdu text classification. The multiclass was categorized into three groups (13 each), i.e., category-1 (1–13), Alif-Zaal (ﺫ - ﺍ), category-2 (14–26), Ray-Fay (ﻒ - ﺮ), and category-3 (27–39), Kaaf-Bri Yay (ے - ﻕ), to give better vision and understanding. The performance was evaluated in terms of true positive rate, true negative rate, positive predictive value, negative predictive value, false positive rate, total accuracy, and area under the receiver operating curve. Among all the classifiers, support vector machine has achieved the highest performance with a 99.73% accuracy. For comparisons, robust machine learning techniques, such as support vector machine, decision tree, and K-nearest neighbors were used. Currently, this work has been done on only Grade 1 Urdu Braille. In the future, we plan to enhance this work using Grade 2 Urdu Braille with text and speech feedback on touchscreen-based android phones.

Becoming an Expert Teacher: Assessing Expertise Growth in Peer Feedback Video Recordings by Lexical Analysis

Teacher education enables students to grow from ‘novice’ into ‘starting expert’ teachers. In this study, students’ textual peer feedback on video recordings of their teaching practice was analysed to determine the growth of their expertise in relation to blended curriculum design. The degree to which curriculum and literature influenced their feedback was assessed by semantic network analysis of prominent words from the literature that was studied, as well as the lexical richness and semantic cohesion of students’ peer feedback and reflections. The lexical richness and the semantic cohesion increased significantly by the end of the semester. This means that students incorporated new vocabulary and maintained semantic consistency while using the new words. Regarding the semantic network analysis, we found stronger connections between the topics being discussed by the students at the end of the semester. Active use of video and peer feedback enhances students’ active knowledge base, thus furthering effective teaching.

User preferences for and engagement with text messages to support antihypertensive medication adherence: Findings from a pilot study evaluating an emergency department-based behavioral intervention

ObjectiveWe examined users’ preferences for and engagement with text messages delivered as part of an emergency department (ED)-based intervention to improve antihypertensive medication adherence. MethodsWe recruited ED patients with elevated blood pressure for a pilot randomized trial evaluating a medication adherence intervention with text messages. Intervention participants chose text content and frequency, received texts for 45 days, and completed a feedback survey. We defined engagement via responses to texts. We examined participant characteristics associated with text preferences, engagement, and feedback. ResultsParticipants (N = 101) were 57% female and 46% non-White. Most participants (71%) chose to receive both reminder and informational texts; 94% chose reminder texts once per day and 97% chose informational texts three times per week. Median text message response rate was 56% (IQR 26–80%). Participants who were Black (p < 0.01), had lower income (p = 0.03), or had lower medication adherence (p < 0.01) rated the program as more helpful and wanted additional functionalities for adherence support. Conclusions and Practice ImplicationsWhile overall engagement was modest, participants at risk of worse health outcomes expressed more value and interest in the program. Findings inform the design of text messaging interventions for antihypertensive medication adherence and support targeting vulnerable patients to reduce health disparities. Clinical trials registrationNCT02672787

Virtual Education Program to Support Providers Caring for People With Intellectual and Developmental Disabilities During the COVID-19 Pandemic: Rapid Development and Evaluation Study.

BackgroundPeople with intellectual and developmental disabilities are at increased health-related risk due to the COVID-19 pandemic. Virtual training programs that support providers in caring for the physical and mental health needs of this population, as well provide psychological support to the providers themselves, are needed during the pandemic.ObjectiveThis paper describes the design, delivery, and evaluation of a virtual educational COVID-19–focused Extension for Community Healthcare Outcomes program to support providers during the COVID-19 pandemic in caring for the mental health of people with intellectual and developmental disabilities.MethodsA rapid design thinking approach was used to develop a 6-session program that incorporates mindfulness practice, a wellness check, COVID-19–related research and policy updates, a didactic presentation on a combination mental health and COVID-19 related topic, and a case-based discussion to encourage practical learning. We used the first 5 outcome levels of Moore’s evaluation framework—focusing on participation, satisfaction, learning, self-efficacy, and change in practice—which were rated (out of 5) by care providers from health and disability service sectors, as well as additional reflection measures about innovations to the program. Qualitative feedback from open-text responses from participants were analyzed using modified manifest content analysis.ResultsA total of 104 care providers from health and disability service sectors participated in the program. High levels of engagement (81 participants per session on average) and satisfaction (overall satisfaction score: mean 4.31, SD 0.17) were observed. Self-efficacy (score improvement: 19.8%), support, and coping improved. Participants also rated the newly developed COVID-19 program and its innovative components highly. Open text feedback showed participants felt that the Extension for Community Healthcare Outcomes program expanded their knowledge and competency and created a sense of being part of a community of practice; provided value for the COVID-19 innovations; supported resource-sharing within and beyond program participants; and facilitated changes to participants’ approaches to client care in practice and increased participants’ confidence in supporting clients and families.ConclusionsThe Extension for Community Healthcare Outcomes program is an effective model for capacity-building programs with a shared-learning approach. Future iterations should include targeted evaluation of long-term outcomes such as staff burnout.

Design of a multifaceted strategy based on automated text messaging in patients with recent heart failure admission.

AimsTo evaluate a telemonitoring strategy based on automated text messaging and telephone support after heart failure (HF) hospitalization.Methods and resultsThe MESSAGE‐HF study is a prospective multicentre, randomized, nationwide trial enrolling patients from 30 clinics in all regions of Brazil. HF patients with reduced left ventricular ejection fraction (<40%) and access to mobile phones are eligible after an acute decompensated HF hospitalization. Patients meeting eligibility criteria undergo an initial feasibility text messaging assessment and are randomized to usual care or telemonitoring intervention. All patients receive a HF booklet with basic information and recommendations about self‐care. Patients in the intervention group receive four daily short text messages (educational and feedback) during the first 30 days of the protocol to optimize self‐care; the feedback text messages from patients could trigger diuretic adjustments or a telephone call from the healthcare team. After 30 days, the frequency of text messages can be adjusted. Patients are followed up after 30, 90, and 180 days, with final status ascertained at 365 days by telephone. Our primary endpoint is the change in N‐terminal pro‐brain natriuretic peptide (NT‐proBNP) levels after 180 days. Secondary endpoints include changes in NT‐proBNP after 30 days; health‐related quality of life, HF self‐care, and knowledge scales after 30 and 180 days; and a composite outcome of HF hospitalization and cardiovascular death, adjudicated by a blinded and independent committee.ConclusionsThe MESSAGE‐HF trial is evaluating an educational and self‐care promotion strategy involving a simple, intensive, and tailored telemonitoring system. If proven effective, it could be applied to a broader population worldwide.

Effect of video feedback on students’ feedback use in the online learning environment

ABSTRACT Feedback is an essential element of education in raising student learning and performance. Along with the ubiquitous use of technologies in education, video feedback has emerged recently as a new mode of feedback delivery. While there are a number of previous studies on perceptions about video feedback, its effectiveness on feedback use remains underexplored. To address this gap, this study uses an experimental approach to investigate the effect of video feedback on students’ feedback use in the online learning environment. A total of 43 undergraduate students participated in the study. After they upload their assignments to the LMS, the experimental group received video feedback and the control group received text feedback by the instructor. After that, students were asked to revise their assignments by considering the feedback. Findings revealed a significant difference in favour of the experimental group and students receiving video feedback become more successful in using feedback.

Enhancing text classification performance by preprocessing misspelled words in Indonesian language

Supervised learning using shallow machine learning methods is still a popular method in processing text, despite the rapidly advancing sector of unsupervised methodologies using deep learning. Supervised text classification for application user feedback sentiments in Indonesian Language is one of the applications which is quite popular in both the research community and industry. However, due to the nature of shallow machine learning approaches, various text preprocessing techniques are required to clean the input data. This research aims to implement and evaluate the role of Levenshtein distance algorithm in detecting and preprocessing misspelled words in Indonesian language, before the text data is then used to train a user feedback sentiment classification model using multinomial Naïve Bayes. This research experimented with various evaluation scenarios, and found that preprocessing misspelled words in Indonesian language using the Levenshtein distance algorithm could be useful and showed a promising 8.2% increase on the accuracy of the model’s ability to classify user feedback text according to their sentiments.

SENTIMENT ANALYSIS OF CUSTOMER REVIEWS

Sentiment analysis is a process in which we classify text data as positive, negative, or neutral or into some other category, which helps understand the sentiment behind the data. Mainly machine learning and natural language processing methods are combined in this process. One can find customer sentiment in reviews, tweets, comments, etc. A company needs to evaluate the sentiment behind the reviews of its product. Customer sentiment can be a valuable asset to the company. This ultimately helps the company make better decisions regarding its product marketing and improving product quality. This paper focuses on the sentiment analysis of customer reviews from Amazon. The reviews contain textual feedback along with a rating system. The aim is to build a supervised machine learning model to classify the review as positive or negative. As reviews are in the text format, there is a need to vectorize the text to numerical format for the computer to process the data. To do this, we use the Bag-of-words model and the TF-IDF (Term Frequency-Inverse Document Frequency) model. These two models are related to each other, and the aim is to find which model performs better in our case. The problem in our case is a binary classification problem; the logistic regression algorithm is used. Finally, the performance of the model is calculated using a metric called the F1 score.

Incorporating the Time–Order Effect of Feedback in Online Auction Markets through a Bayesian Updating Model

Online auction markets host a large number of transactions every day. The transaction data in auction markets are useful for understanding the buyers and sellers in the market. Previous research has shown that sellers with different levels of reputation, as shown by the ratings and comments left in feedback systems, enjoy different levels of price premiums for their transactions. Feedback scores and feedback texts have been shown to correlate with buyers’ level of trust in a seller and the price premium that buyers are willing to pay (Ba and Pavlou 2002; Pavlou and Dimoka 2006). However, existing models do not consider the time-order effect, which means that feedback posted more recently may be considered more important than feedback posted less recently. This paper addresses this shortcoming by (1) testing the existence of the time-order effect, and (2) proposing a Bayesian updating model to represent buyers’ perceived reputation considering the time-order effect and assessing how well it can explain the variation in buyers’ trust and price premiums. In order to validate the time-order effect and evaluate the proposed model, we conducted a user experiment and collected real-life transaction data from the eBay online auction market. Our results confirm the existence of the time-order effect and the proposed model explains the variation in price premiums better than the benchmark models. The contribution of this research is threefold. First, we verify the time-order effect in the feedback mechanism on price premiums in online markets. Second, we propose a model that provides better explanatory power for price premiums in online auction markets than existing models by incorporating the time-order effect. Third, we provide further evidence for trust building via textual feedback in online auction markets. The study advances the understanding of the feedback mechanism in online auction markets.

Developing a virtual mock casc for trainees

AimsThe restrictions experienced due to the COVID-19 pandemic had impacts on how clinical teaching and assessment is conducted. The Royal College of Psychiatrists decided to run the final part of the membership exam, the Clinical Assessment of Skills and Competencies (CASC) online for the first time in September 2020. We aimed to prepare candidates in the Northern Ireland deanery for this by developing a virtual mock examination using the Zoom platform.MethodIn previous years, higher psychiatry trainees in the Northern Ireland deanery have run successful face to face mock examinations to help pre-membership trainees prepare for the CASC. We adapted some of this material to our virtual examination. 16 stations were run in total, in two circuits of eight. These stations were mapped to the Royal College CASC blueprint. Higher trainees were recruited to act as examiners, with core trainees acting as simulated patients. The mock examination was advertised through the local deanery and all candidates sitting in September availed of the opportunity (a total of 8 trainees).Zoom was used as the platform due to ease of use, familiarity and breakout room function. Each station formed one breakout room, and a facilitator moved candidates between rooms and provided timing prompts. Instructions were emailed to candidates in advance.A comfort break was provided between circuits. At the end of the mock examination, everyone was returned to the main room and examiners gave general feedback and tips. Individual feedback was provided by collating mark schemes for each candidate, which included free text feedback, and sending these via email.ResultDespite the evident challenges involved, the mock CASC ran smoothly. There was one minor delay of approximately 3 minutes due to technical difficulties, which was easily recouped. We obtained qualitative feedback from candidates which was positive, with trainees commenting that they felt “more at ease … less worried” about a digital exam, and that it was “efficient and effective”.All candidates who sat the mock examination were successful in the face to face CASC sitting which followed in September.ConclusionWe were able to successfully adapt what was previously an in-person mock CASC exam to the new digital format in a way that reflected how the actual CASC exam will run, and it was considered beneficial preparation by the candidates who sat this mock. This has improved trainee experience at a time when many teaching opportunities have been suspended.

Dr QI - A quality improvement (QI) approach to designing and delivering QI training

AimsTo develop and implement a QI training programme for trainees, Trust grade doctors and Consultants in Nottinghamshire Healthcare NHS Trust (NHFT) to enable them to deliver change in practice through acquisition of new knowledge and practical application of skills in QI projects using Model for Improvement.BackgroundQI is crucial to improve patient care. Doctors are uniquely placed to input into patient safety and service delivery of healthcare. The skills required to be future clinical leaders and undertake improvement work are not innate and formal teaching and support is required.What is DrQI?DrQI is a trainee-led QI teaching programme developed in collaboration with Trainees improving patient safety through QI (TIPSQI) in North West deanery.MethodA pre-implementation survey amongst doctors in NHFT in February 2019 (33 responses) suggested that 90% of doctors were interested in learning QI and about 48% preferred face-face workshops with support from the QI team.A list of change ideas were created using a driver diagram with QI education and project support identified as key primary drivers.PDSA cyclesNine interactive workshops teaching key QI concepts (based on model for improvement) in NHFT, training more than 100 doctors. A workshop in Derbyshire Healthcare NHS Foundation Trust (70 doctors) and Nottingham University Hospital (20 doctors). Workshops were continually adapted based on qualitative and quantitative feedback. Different formats were tried including virtual sessions, game-based and problem-based learning approaches using small group activities.ResultPre-course and post-course questionnaires were used to assess change in understanding of individual components of QI methodology (SMART Aim, Driver diagram, PDSA cycles, outcome and process measures and run charts). Mean pre-course self-assessment score collated from seven QI workshops in NHFT (2019-2020) was 3.3 and mean post-course score was 7.68, showing an improvement in understanding of QI methodology.Participants were asked to score the relevance (8.4) and quality of teaching (8.4) and the support from the QI team (7.4) on a scale of 1-10 (1 = poor and 10 = excellent). Additional free text feedback was obtained to help us improve the teaching programme.ConclusionCollaborative leadership trainee-led initiative to increase the QI capacity. A bottom up approach to complement the top down approach from the Trust QI team. Future steps include further collaboration and expansion of the scheme to other Trusts, Train the trainer sessions and building a network of QI champions.

Establishing Continuous App Improvement by Considering Heterogenous Data Sources

&lt;p class="0abstract"&gt;&lt;span lang="EN-US"&gt;Mobile apps have penetrated the market and are being used every­where. Companies developing apps face increasing challenges such as short time to market or demand for high quality. Furthermore, the success of an app also depends on how users perceive its quality. Feedback provided by users influences other potential users and provides new opportunities for identifying features. Consequently, it is a valuable source of input for app developers with respect to product improvements. One form is textual feedback. This kind of feedback is usually distributed across various data sources. Therefore, it must be captured from these sources and put into one single pool of data before it can be analyzed. The analysis must take into account the peculiarities of the short release cycles and high change rate of features for mobile apps. In this article, we present User Echo Service (UES), which was built to address heterogeneous data sources. The aim of UES is to allow product managers to be able to be always up to date with the latest feedback data. Therefore, we have created an extensible architecture aimed at supporting different data sources and present the feedback collection scheduling system. This forms the prerequisite for subsequent analyses of the collected data. We discuss our solution and provide ideas for future development.&lt;/span&gt;&lt;/p&gt;

559 Effect of Weekly Text Message Feedback on Continuous Positive Airway Pressure Adherence in Pediatric Obstructive Sleep Apnea

Abstract Introduction Continuous Positive Airway Pressure (CPAP) is an effective treatment for Obstructive Sleep Apnea (OSA) in children. However, adherence to CPAP therapy is often suboptimal. CPAP telemonitoring with automated feedback messaging has been shown to improved adherence in adults with OSA, but has not been widely studied as an intervention for children with OSA. This pilot study was conducted to investigate if feedback messaging could similarly improve CPAP adherence in children. Methods Pediatric OSA subjects (ages 11–17) with poor CPAP compliance or newly prescribed CPAP were selected from an outpatient Sleep Medicine clinic to receive weekly text messages with scripted feedback on CPAP adherence. Patients already meeting adherence goals were excluded from the study. Parental consent was obtained and participants were given the option to have text messages sent to parents or directly to the patient. Adherence was monitored over a four-month intervention period and compared to pre-intervention usage as a self-matched control. Total hours of use per month and numbers of days used per month were collected through daily remote CPAP monitoring. Average hours per use and average daily use per 30-day period were additional adherence measurements calculated. Data was analyzed using two-tailed paired T-tests with level of significance set at p&amp;lt;0.05. Results There was no significant change in CPAP adherence (p&amp;gt;.05) after initiation of weekly feedback messaging for the five patients included in the study. No participant, before or after intervention, met the Medicare definition of compliance (≥4hr nightly use for ≥ 70% of nights) and average adherence declined following intervention. Conclusion In a small pilot study, weekly feedback text messaging did not improve CPAP adherence in pediatric patients with OSA. This finding contrasts with larger studies in adult patients with OSA that have demonstrated improved CPAP adherence with automated feedback messaging. With the increasing use of telemedicine for CPAP follow-up, new strategies to successfully utilize this approach in the pediatric population may be needed. Support (if any) None.

Medical Student Perceptions of Virtual Pharmacology Educational Games

Previous studies from our institution have demonstrated the value of pharmacology educational games in promoting recall practice and topic interest necessary for medical students to learn pharmacology. At our institution, pharmacology and other basic science topic games are embedded into curricular activities, in-class, faculty facilitated team competitions, and self-learning modules (SLMs). Little work has been done to assess student perceptions of virtual versus in-class games, despite the fact that developing effective virtual pharmacology learning tools has become essential due to curricular changes adapting to the COVID-19 pandemic. The goal of this pilot study was to survey students on their perceptions and preferences for in-class, faculty facilitated games vs. virtual electronic games. Since we hypothesized that students’ perceptions of feedback would differ between in-class and virtual games, we additionally surveyed students on their preferences for enriched auditory vs. simple text feedback within virtual games. Methods An end of module survey was sent to all students in a second-year medical school (M2) systems course (N=110 total, 70% participated) to get a broad class perceptive. A subset of M2's (N=14) volunteered to beta test pharmacology virtual trivia games with feedback that ranged from plain text answers to enriched auditory feedback performed by actors (varied genders, ages, personalities) and feedback from a trusted pharmacology instructor offering coaching and praise similar to classroom feedback. Results Approximately 70% of students reported using the games and answered survey items, with most affirming (reported as % agreeing/strongly agreeing) that virtual and in-class games promoted active learning (80%), integrated basic and clinical concepts (84%), and stimulated thinking (84%). About 60% preferred participating in or watching videos of in-class games with auditory feedback from instructors. Of those with a preference, 50% preferred Trivia while 25% preferred Jeopardy. Of the 14 students beta testing virtual trivia games, most agreed (83%) that the virtual games increased pharmacology interest and promoted active learning, though fewer than 25% preferred enriched auditory feedback from actors over simple written text. A majority (61%) agreed that hearing the voice of a trusted professor was beneficial. Conclusion Most M2 students used game-based methods and highly valued either in-class team or virtual games for promoting topic interest, active learning, and integration. Students valued coaching and auditory feedback from a trusted professor in either classroom or virtual games. However, most students preferred simple text feedback over auditory, unless the feedback voice was the pharmacology instructor's. Thus, virtual pharmacology educational games with simple feedback designs appear to be engaging virtual teaching tools for pharmacology learning in the age of COVID and beyond.

P083 A rheumatology carousel: three-year results from an open-access teaching workshop designed for core rheumatology assessments for undergraduate medical students

Background/Aims Medical students need to gain patient contact to develop their skills in history taking and examinations. In year three, undergraduate students typically rotate across various hospitals and specialties and are expected to have dedicated rheumatology exposure for history and examination competencies. Rheumatology as an out-patient specialty can limit opportunities for medical students to have broad exposure to rheumatological conditions. Methods In January 2018, we designed an annual rheumatology half-day teaching workshop (‘Rheumatology Carousel’) using a combination of lecture-based teaching and small group based guided clinical history and examination stations, aimed at third-year medical students from the University of Birmingham. This covered key presentations in rheumatology: axial spondyloarthropathy, rheumatoid arthritis, systemic sclerosis (connective tissue disease), osteoarthritis, and vasculitis. Each station required a Clinical Teaching Fellow or Rheumatology ST trainee, overseen by one consultant facilitator. We designed patient proforma’s incorporating consent, demographics, key clinical history, therapy, and examination findings. We produced a written patient guide, and consultants invited appropriate patients to volunteer for the day. We designed a one-hour lecture-based tutorial. A lesson plan and schedule were created outlining faculty requirements; including time, roles, and faculty numbers. We invited five to six patients to each session, with a plan of four to five focussed examinations. We designed the carousel to accommodate up to 40 students, split into two groups running over a day. Focussed examinations involved students in groups of four, with each student being a lead examiner in at least one station, each station lasting 20 minutes. Best practice examination techniques for each condition were assessed and emphasised. Following a debrief, we collected feedback from students, faculty, and patients (online and written feedback), using Likert scores for teaching content, and quality of the session delivery. Results The carousel ran in February 2018, 19, and 20. The sessions were positively evaluated by students, faculty, and patients. In total, 93 students attended, 89/93 completed feedback. Satisfaction scores (mean; SD; range) were high (1-strongly disagree, 5-strongly agree) for content (4.8; 0.49, range 3-5) and quality of delivery (4.7; 0.54; 3-5). All patients who participated volunteered to return for future teaching sessions, with several patients attending all three years. Free text feedback indicated students valued structured exposure to core conditions and called for more sessions of this nature. Conclusion This sustainable reproducible intervention ensures students have structured exposure to important rheumatological conditions. The methodology allows reproducible sessions that are positively evaluated despite rotating clinical teaching staff. We have made all our teaching materials, logistical plan, and scheduling tools available as open access resources under a Creative Commons license for free re-use and adaptation by any healthcare professional, via a web link. We plan to record an electronic version to distribute post the COVID-19 pandemic. Disclosure P. Mistry: None. J. Bateman: None. H. Foss: None. M. Jasim: None.

Daily Motivational Text Messages to Promote Physical Activity in University Students: Results From a Microrandomized Trial.

Low physical activity is an important risk factor for common physical and mental disorders. Physical activity interventions delivered via smartphones can help users maintain and increase physical activity, but outcomes have been mixed. Here we assessed the effects of sending daily motivational and feedback text messages in a microrandomized clinical trial on changes in physical activity from one day to the next in a student population. We included 93 participants who used a physical activity app, "DIAMANTE" for a period of 6 weeks. Every day, their phone pedometer passively tracked participants' steps. They were microrandomized to receive different types of motivational messages, based on a cognitive-behavioral framework, and feedback on their steps. We used generalized estimation equation models to test the effectiveness of feedback and motivational messages on changes in steps from one day to the next. Sending any versus no text message initially resulted in an increase in daily steps (729 steps, p = .012), but this effect decreased over time. A multivariate analysis evaluating each text message category separately showed that the initial positive effect was driven by the motivational messages though the effect was small and trend-wise significant (717 steps; p = .083), but not the feedback messages (-276 steps, p = .4). Sending motivational physical activity text messages based on a cognitive-behavioral framework may have a positive effect on increasing steps, but this decreases with time. Further work is needed to examine using personalization and contextualization to improve the efficacy of text-messaging interventions on physical activity outcomes. NCT04440553.