Topic Summaries Research Articles

Cancer genetic counseling via telegenetics and telephone: A qualitative study exploring the experience of patients and genetic counselors in an Australian cancer genetics context.

The demand for direct-to-patient (DTP) telegenetics (genetics services delivered via videoconferencing) in genetic counseling practice has rapidly increased, particularly since the COVID-19 pandemic. Recent telegenetics literature is mostly quantitative and not in the Australian context. A qualitative interview study was conducted to address this gap. This research investigated the experiences of patients and genetic counselors (GCs), enrolled in a randomized controlled trial, using telegenetics and telephone for cancer genetic counseling appointments. Twenty-eight semi-structured interviews with patients (n = 22) and GCs (n = 6) were conducted following patient randomization to either a telephone or telegenetics genetic counseling appointment. The interviews explored participant's experiences of telegenetics and compared DTP telegenetics with telephone and in-person delivery. Codebook thematic analysis was used to develop topic summaries from the data. Patient and GC participants noted positive experiences of telegenetics; with key benefits reported as reduced travel time, time and cost saving, ease, convenience, efficiency, and comfortability. Technical issues and privacy concerns were highlighted as potential disadvantages of telegenetics. All but one patient felt sufficiently emotionally supported while using telegenetics. Telegenetics has both benefits and limitations; however, generally, this cohort found telegenetics to be a suitable and acceptable mode of delivery for genetic counseling with many advantages over in-person or telephone appointments. Further studies should be conducted to provide evidence for the long-term implementation of telegenetics, regardless of any future COVID-19 pandemic lockdown restrictions.

A critical review of the reporting of reflexive thematic analysis in Health Promotion International.

Using the concept of methodological congruence-where the different elements of a study 'fit' together-we explore both problematic and good practice in (reflexive) thematic analysis (TA) as reported in Health Promotion International (HPI). Aligning with the importance we place on 'owning your perspectives' we situate this exploration in relation to our understanding of the variation in approaches to TA and qualitative research more broadly. This contextualization is necessary for highlighting why we judge practices to be in/congruent, and to facilitate more knowing congruence in future research. We critically reviewed a 'sample' of 31 papers published in HPI between 2010 and 2023 citing Braun and Clarke as reference for TA. We overview a range of problematic and good features of the use of TA in HPI, before focusing on two domains that seemed to present key challenges: theory and themes. Methodological incongruence can occur when postpositivist values and practices unwittingly creep into ostensibly non-positivist TA; we encourage thoughtfully and what we term 'knowing' consideration of theory, and quality practices and criteria. Methodological incongruence can also occur through mismatched conceptualizations of themes-notably, the use of 'topic summaries' as themes for reflexive TA (and fragmented thematic structures with 'thin' themes). We provide examples from the reviewed papers to demonstrate good practice in researcher reflexivity, articulation of theoretical and methodological frameworks and congruent themes. However, mindful of power dynamics, we only discuss problematic practice in general terms, to protect author anonymity. To facilitate thoughtful, quality TA-of all kinds-we provide eight pointers for researchers (and reviewers) to guide quality practice, and facilitate the use of concepts, procedures and criteria that promote knowing methodological congruence.

Premature Closure of Analysis in Qualitative Research: Identifying Features and Mitigation Strategies

Premature closure of analysis refers to finishing data analysis too early, leading to underdeveloped qualitative findings. It is a critical issue in qualitative research affecting the rigor and trustworthiness of qualitative findings. While much has been written about how to conduct rigorous data analysis across a range of qualitative approaches, there has been no discussion of the features of premature closure of analysis and strategies for addressing it. The purpose of this paper is to outline how to spot premature closure of analysis and to describe strategies to mitigate this issue. Three identifying features of premature analysis are: providing thin descriptions with loaded participant quotes, presenting conventional concepts as themes, and using topic summaries as themes. Using a First Approach to qualitative analysis, working in segments to generate a wholistic thematic output, and critical reflection and examination before finalizing the thematic output can be useful strategies to mitigate premature closure of analysis. Themes and patterns that are too vague and meaningless to provide a comprehensive account of the studied phenomenon are a threat to the validity of the study and a waste of researchers’ effort and time. Premature closure of analysis is one of the most common problems affecting the quality of thematic outputs in quality studies. Therefore, researchers should be mindful and critical in their analytical decision-making to prevent this problem.

Roles, mutual expectations and needs for improvement in the care of residents with (a risk of) dehydration: A qualitative study.

Examining the perspectives of formal and informal caregivers and residents on roles, mutual expectations and needs for improvement in the care for residents with (a risk of) dehydration. Qualitative study. Semi-structured interviews with 16 care professionals, three residents and three informal caregivers were conducted between October and November 2021. A thematic analysis was performed on the interviews. Three topic summaries contributed to a comprehensive view on the care for residents with (a risk of) dehydration: role content, mutual expectations and needs for improvement. Many overlapping activities were found among care professionals, informal caregivers and allied care staff. While nursing staff and informal caregivers are essential in observing changes in the health status of residents, and medical staff in diagnosing and treating dehydration, the role of residents remains limited. Conflicting expectations emerged regarding, for example, the level of involvement of the resident and communication. Barriers to multidisciplinary collaboration were highlighted, including little structural involvement of allied care staff, limited insight into each other's expertise and poor communication between formal and informal caregivers. Seven areas for improvement emerged: awareness, resident profile, knowledge and expertise, treatment, monitoring and tools, working conditions and multidisciplinary working. In general, many formal and informal caregivers are involved in the care of residents with (a risk of) dehydration. They depend on each other's observations, information and expertise which requires an interprofessional approach with specific attention to adequate prevention. For this, educational interventions focused on hydration care should be a core element in professional development programs of nursing homes and vocational training of future care professionals. The care for residents with (a risk of) dehydration has multiple points for improvement. To be able to adequately address dehydration, it is essential for formal and informal caregivers and residents to address these barriers in clinical practice. In writing this manuscript, the EQUATOR guidelines (reporting method SRQR) have been adhered to. No patient or public contribution.

The Perceptions of Elite Professional Rugby League Players and Staff on the National Rugby League Annual Calendar: A Mixed-Methods Study

BackgroundIn recent years, the length of elite sporting competitions has raised concerns regarding player well-being, highlighting a need to review current match calendars. Therefore, this study aimed to explore the perceptions of elite National Rugby League (NRL) players and staff on the annual training and competition calendar from a player workload and well-being perspective.MethodsThis study adopted a mixed-methods approach, using a sequential explanatory design. Phase one implemented a cross-sectional survey, and phase two utilised semi-structured interviews. Four hundred and thirty-nine elite rugby league players and 46 staff completed the survey. Eighteen elite professional NRL players and six football staff were interviewed, and verbal data were analysed into pre-defined topic summaries using qualitative coding reliability methods. Topics included in-season, off-season, pre-season and well-being.ResultsData analysis suggests that elite NRL players and staff believe players appear particularly comfortable with the current number of games; however, they are at their maximum capacity. Importantly, this study identified several minority groups that may require support to enhance player well-being. Players believe reducing the pre-season would negate fatigue experienced later in the subsequent season. Players and staff believe this timeframe still provides sufficient time to prepare for the upcoming season. Further, players were open to extending the off-season to 8–10 weeks and believed that extra time would allow for greater recovery from the previous season. Mid-season congested scheduling affects players following the intensified period and requires attention to alleviate fatigue.ConclusionThe results of this study convey important implications for the NRL, emphasising a need to review their annual training and competitive calendar, or to implement specific strategies to enhance the well-being of minority groups. The findings from this study should be considered when discussing the ideal length and structure of the match calendar to support players’ physical and mental welfare.

Therapist Feedback and Implications on Adoption of an Artificial Intelligence-Based Co-Facilitator for Online Cancer Support Groups: Mixed Methods Single-Arm Usability Study.

The recent onset of the COVID-19 pandemic and the social distancing requirement have created an increased demand for virtual support programs. Advances in artificial intelligence (AI) may offer novel solutions to management challenges such as the lack of emotional connections within virtual group interventions. Using typed text from online support groups, AI can help identify the potential risk of mental health concerns, alert group facilitator(s), and automatically recommend tailored resources while monitoring patient outcomes. The aim of this mixed methods, single-arm study was to evaluate the feasibility, acceptability, validity, and reliability of an AI-based co-facilitator (AICF) among CancerChatCanada therapists and participants to monitor online support group participants' distress through a real-time analysis of texts posted during the support group sessions. Specifically, AICF (1) generated participant profiles with discussion topic summaries and emotion trajectories for each session, (2) identified participant(s) at risk for increased emotional distress and alerted the therapist for follow-up, and (3) automatically suggested tailored recommendations based on participant needs. Online support group participants consisted of patients with various types of cancer, and the therapists were clinically trained social workers. Our study reports on the mixed methods evaluation of AICF, including therapists' opinions as well as quantitative measures. AICF's ability to detect distress was evaluated by the patient's real-time emoji check-in, the Linguistic Inquiry and Word Count software, and the Impact of Event Scale-Revised. Although quantitative results showed only some validity of AICF's ability in detecting distress, the qualitative results showed that AICF was able to detect real-time issues that are amenable to treatment, thus allowing therapists to be more proactive in supporting every group member on an individual basis. However, therapists are concerned about the ethical liability of AICF's distress detection function. Future works will look into wearable sensors and facial cues by using videoconferencing to overcome the barriers associated with text-based online support groups. RR2-10.2196/21453.

Risk and resilience: exploring the potential of LGBTQ third sector and academic partnership

Abstract The Risk and Resilience Explored [RaRE] Project (2010–2016) was a collaborative process involving a third sector agency, university partners and volunteers to better understand the risk and resilience factors associated with specific mental health issues among lesbian, gay, bisexual, trans, and queer (LGBTQ) people. In this article, we discuss the project’s collaborative ethos, based on a Community-Based Participatory Research (CBPR) approach. We explain how the CBPR approach benefitted from including academic partners from the onset of the project, as well as from the direct and indirect engagement of community volunteers. We then explore some of our experience of third sector and academic partner collaboration in more depth, highlighting topic summaries salient to this partnership: support and continuity, upskilling of staff and volunteers for mutual benefit, accessible communication across sectors, and aligning priorities. We conclude by setting out recommendations based on our experience for those interested in developing similarly collaborative projects.

A longitudinal qualitative multi-methods study of new dental graduates' transition journey from undergraduate studies to professional practice.

New dental graduates' (NDGs) transition to professional practice is an important landmark and developmental stage in their career and in the UK it is supported by a formal educational transition process through a one-year salaried practice-based programme. However, little is known about graduates' experiences during this period. As part of a larger mixed-methods project, this study aimed to explore NDGs' experiences during their transition to professional practice, represented by Vocational Dental Practice. Sixty-six NDGs from one dental school were invited to participate. Two rounds of in-depth interviews were conducted: Interview 1 upon NDGs' graduation and then Interview 2 was a follow-up interview after they had spent six-nine months in vocational dental training (VDT). At Interview 1, a subset of participants agreed to record longitudinal audio-diaries (LADs) and continued these for 6-9 months into VDT. Data from the interviews and LADs were analysed together using a thematic analysis approach. Eleven of the 66 invited NDGs agreed to be interviewed at Interview 1 (16.6%), seven at Interview2 (10.6%), and six (9.2%) recorded LADs. Four topic summaries were developed around NDGs transition experiences: (1) conceptualisation of the process; (2) responses to the transition, (3) challenges and support and (4) relationships with stakeholders. NDGs' transition to professional practice was perceived as exciting and rewarding on personal and professional levels but carried challenges. VDT and related stakeholders play a vital role of supporting NDGs into their new professional life.

“Walking the Journey Together”: Creating a unique learning module in provider-patient communication for the care of epilepsy in Uganda

ObjectiveThis report documents the creation of a practical communication skills module about epilepsy care, specifically targeted at first-line care providers who treat patients with epilepsy in Uganda. MethodsOur team conducted semi-structured interviews, utilizing Zoom video conferencing, with Ugandan physicians specializing in epilepsy care. Our interview guide promoted a semi-structured conversational interview that explored aspects related to developing a patient-provider relationship, how epilepsy is described in a culturally appropriate manner, exploration of alternative treatments, the impact of the stigma of epilepsy, and facilitators and barriers to antiepileptic drug treatment adherence. Each interview was then transcribed, and an inductive thematic content analysis approach was utilized to facilitate the development of thematic communication and care subcategories. The resulting PowerPoint presentation included numerous short audio clips of our Ugandan experts suggesting effective ways of communicating with patients and their families. ResultsOur interviews with experts yielded valuable results to customize the WHO mhGAP v2.0 training program to be culturally relevant and effective in Uganda. The educational content consisted of topic summaries integrated with audio clips taken directly from our interviews with the Ugandan providers. Six themes emerged that would serve as the outline for the communication module we co-created with our Ugandan colleagues: The six major themes of the module included: (1) Greeting the patient, (2) Getting the story, (3) Traditional healers, (4) Stigma of epilepsy, (5) Explaining epilepsy, and (6) Treatment adherence. ConclusionsThe communications skills teaching module addresses the most critical aspects of communicating with patients and families living with epilepsy. The format of the presentation, which includes the written and spoken words of experts in epilepsy care, provides a practical approach to the provider-patient interaction, and confronts the stigma associated with this disease. This formatting highlights an effective way for international groups to co-create content in a culturally effective manner.

What's New in Orthopaedic Trauma.

What's New in Orthopaedic Trauma.

Hierarchical Stream Clustering Based NEWS Summarization System

News feed is one of the potential information providing sources which give updates on various topics of different domains. These updates on various topics need to be collected since the domain specific interested users are in need of important updates in their domains with organized data from various sources. In this paper, the news summarization system is proposed for the news data streams from RSS feeds and Google news. Since news stream analysis requires live content, the news data are continuously collected for our experimentation. The major contributions of this work involve domain corpus based news collection, news content extraction, hierarchical clustering of the news and summarization of news. Many of the existing news summarization systems lack in providing dynamic content with domain wise representation. This is alleviated in our proposed system by tagging the news feed with domain corpuses and organizing the news streams with the hierarchical structure with topic wise representation. Further, the news streams are summarized for the users with a novel summarization algorithm. The proposed summarization system generates topic wise summaries effectively for the user and no system in the literature has handled the news summarization by collecting the data dynamically and organizing the content hierarchically. The proposed system is compared with existing systems and achieves better results in generating news summaries. The Online news content editors are highly benefitted by this system for instantly getting the news summaries of their domain interest.

Systematic Mapping Study of Enterprise Resource Planning

Enterprise resource planning needs to be maximized in line with technological developments and circumstances that can change unexpectedly. The goal is to integrate business processes to enhance collaboration. Organizational performance and critical success factors have become important in enterprise resource planning from 2011 to 2020. This article aims to classify, identify scientific publications, and thematic analysis of the latest literature to create a broad and detailed understanding of enterprise resource planning. The research method used is a systematic mapping study (SMS) to examine scientific publications produced from time to time, types of research, and methods. The SMS procedure follows established empirical guidelines and the mapping data relies on the IEEE, ScienceDirect, and Scopus electronic databases. Based on the results of SMS, it is known that 89 studies met the inclusion criteria. 89 articles were classified by type of paper, method, focus, locus, and year of publication. Then, categorization and quantification of current studies are generated on various dimensions, topic summaries, and current research trends.

Geriatric Fast Facts: Four Years of Growth and Reach

Clinical teachers are increasingly challenged to find the time to provide point of care education for learners. To meet this challenge, an interprofessional team from competing health care systems created and sustained Geriatric Fast Facts (GFF): easily accessible, concise 1-2 page topic summaries for clinical teachers to use (in lieu of the mini-lecture) with learners at the point of care. Designed by geriatrics educators in consultation with IT experts, we launched a mobile enabled website that is indexed and searchable by free text or topic, organ system, ACGME competency, disease, and the “underlying science” for the disease/illness. GFF topics are authored by subject matter experts with peer review by senior geriatricians. Brief quizzes test learners’ knowledge with score reports. Our 4-year results reveal that: 1) “Geriatric Fast Facts” appear in the top Google 10 listing; 2) Search engines account for 39% of site traffic; 3) 60% of unique users (N=29,000) are via direct link; 4) 19% via referral from another site. Our “bounce rate” (55%) is ideal as users quickly gain the information sought - affirmed by our session duration which averages < 2 minutes. Our Google Analytics results reflect steady growth and international reach: 73% of sessions are from the U.S., 5% from Canada, 22% international and growing! In summary with our population continuing to age and time for teaching being limited, GFFs are quick, accessible, evidence-based resources for point of care teaching.

A Semantically Rich Framework for Knowledge Representation of Code of Federal Regulations

Federal government agencies and organizations doing business with them have to adhere to the Code of Federal Regulations (CFR). The CFRs are currently available as large text documents that are not machine processable and so require extensive manual effort to parse and comprehend, especially when sections cross-reference topics spread across various titles. We have developed a novel framework to automatically extract knowledge from CFRs and represent it using a semantically rich knowledge graph. The framework captures knowledge in the form of key terms, rules, topic summaries, relationships between various terms, semantically similar terminologies, deontic expressions, and cross-referenced facts and rules. We built our framework using deep learning technologies like TensorFlow for word embeddings and text summarization, Gensim for topic modeling, and Semantic Web technologies for building the knowledge graph. In this article, we describe our framework in detail and present the results of our analysis of the Title 48 CFR knowledge base that we have built using this framework. Our framework and knowledge graph can be adopted by federal agencies and businesses to automate their internal processes that reference the CFR rules and policies.

Language Model-Driven Topic Clustering and Summarization for News Articles

Topic models have been widely utilized in Topic Detection and Tracking tasks, which aim to detect, track, and describe topics from a stream of broadcast news reports. However, most existing topic models neglect semantic or syntactic information and lack readable topic descriptions. To exploit semantic and syntactic information, Language Models (LMs) have been applied in many supervised NLP tasks. However, there are still no extensions of LMs for unsupervised topic clustering. Moreover, it is difficult to employ general LMs (e.g., BERT) to produce readable topic summaries due to the mismatch between the pretraining method and the summarization task. In this paper, noticing the similarity between content and summary, first we propose a Language Model-based Topic Model (LMTM) for Topic Clustering by using an LM to generate a deep contextualized word representation. Then, a new method of training a Topic Summarization Model is introduced, where it is not only able to produce brief topic summaries but also used as an LM in LMTM for topic clustering. Empirical evaluations of two different datasets show that the proposed LMTM method achieves better performance over four baselines for JC, FMI, precision, recall and F1-score. Additionally, the generated readable and reasonable summaries also validate the rationality of our model components.

Topic Summary Views for Exploration of Large Scholarly Datasets

In this article, we present the E-sch approach for exploration of large scholarly datasets based on topic summary views. The goal of E-sch is to semantically summarize the dataset related to a potentially very large number of scholar publications (e.g., millions) by a list of few thousands topics, up to an ultimate list of hundreds of topic summaries to use for analyzing research dynamics and evolution at a more semantic, high-level of inquiry. Filter and Slice operators are defined in E-sch to enforce interactive scholarly data exploration along thematic and temporal perspectives.

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework.

Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.

Deloitte Human Capital Trends in Perspective: The Science of Organization Design and Operation

Deloitte and supporting organization, Bersin, are arguably the world’s foremost institutions in researching and offering comprehensive annual reports on the status of modern thinking, and the trends in that thinking, in all facets of organizational operations and performance. The latest 2017 report, the fifth in the series, is perhaps their most thorough to date, and offers global leading assessment of modern thinking and the manner it is changing. What is lacking is any scientific foundation, a lack not of Deloitte, but of global understanding of people in organizations, that is, the lack lies in the foundations of global social science. The question posed by the Deloitte report and all such efforts is: What if there was created a fully scientific theory of people, with a derived theory of people in organizations, how would that modify the report and its implications? OPD-Theory™ is exactly such a theory, derived from the only general theory of psychology, itself derived by applying a revised scientific methodology to the system ‘a person in their environment’. The technology derived from the science is OPD human capital development (OPD-HCD™ ). This paper presents the 2017 Deloitte topic summaries and describes how the OPD-HCD™ provides the solution for making more precise, and implementing, all scientifically valid projections in the report, and corrects those not scientifically valid.

Improving and Evaluating Topic Models and Other Models of Text

ABSTRACTAn ongoing challenge in the analysis of document collections is how to summarize content in terms of a set of inferred themes that can be interpreted substantively in terms of topics. The current practice of parameterizing the themes in terms of most frequent words limits interpretability by ignoring the differential use of words across topics. Here, we show that words that are both frequent and exclusive to a theme are more effective at characterizing topical content, and we propose a regularization scheme that leads to better estimates of these quantities. We consider a supervised setting where professional editors have annotated documents to topic categories, organized into a tree, in which leaf-nodes correspond to more specific topics. Each document is annotated to multiple categories, at different levels of the tree. We introduce a hierarchical Poisson convolution model to analyze these annotated documents. A parallelized Hamiltonian Monte Carlo sampler allows the inference to scale to millions of documents. The model leverages the structure among categories defined by professional editors to infer a clear semantic description for each topic in terms of words that are both frequent and exclusive. In this supervised setting, we validate the efficacy of word frequency and exclusivity at characterizing topical content on two very large collections of documents, from Reuters and the New York Times. In an unsupervised setting, we then consider a simplified version of the model that shares the same regularization scheme with the previous model. We carry out a large randomized experiment on Amazon Mechanical Turk to demonstrate that topic summaries based on frequency and exclusivity, estimated using the proposed regularization scheme, are more interpretable than currently established frequency-based summaries, and that the proposed model produces more efficient estimates of exclusivity than the currently established models.

Is peer review an appropriate form of assessment in a MOOC? Student participation and performance in formative peer review

Many aspects of higher education must be reconceptualised for massive open online courses (MOOCs). Formative and summative assessment of qualitative work in particular requires novel approaches to cope with the numbers involved. Peer review has been proposed as one solution, and has been widely adopted by major MOOC providers, but there is currently little evidence about whether it is appropriate or under what conditions. Here, we examine student participation, performance and opinions of a peer review task in a biomedical science MOOC. We evaluate data from approximately 200 student topic summaries and 300 qualitative peer reviews of those summaries, and compare these to student demographic data (gender, age, employment status, education, national language) and to performance in multiple choice tests. We show that higher performance in the written topic summary correlated with both higher participation in the peer review task and with writing higher quality peer reviews. Qualitative analysis of student comments revealed that student opinion on the usefulness of the peer review task was mixed: some strongly believed it benefitted their learning, while others did not find it useful or did not participate. We suggest instructional design strategies to improve student participation and increase learning gain from peer review in the MOOC context.