Statistical literacy in obstetricians and gynecologists

The Obstetrician-Gynecologist Statistical Literacy Questionnaire (OGSLQ) was designed to examine physicians' understanding of various number tasks that are relevant to obstetrician-gynecologists (ob-gyns) practice. Forty-seven percent of the nationally representative, practicing ob-gyns responded. Physicians did poorly on the questions about numerical facts (e.g., number of women living with HIV/AIDS), better on questions about statistical concepts (e.g., incidence, prevalence), and best on questions about numerical relationships (e.g., convert frequency to percentage) with 0%, 7%, 36%, answering all correctly, respectively. Only 19% correctly estimated the number of U.S. women with cancer. Sixty-six percent were able to use sensitivity and specificity to choose a test option. Around 90% could translate between frequency and probability formats. Forty-nine percent of respondents were able to calculate the positive predictive value of a mammography screening test. Physicians lack some understanding of statistical literacy. It is important that we monitor physicians' statistical literacy and provide training to students and physicians.

Measuring Mental Workload With Low-Cost and Wearable Sensors: Insights Into the Accuracy, Obtrusiveness, and Research Usability of Three Instruments

The affordability of wearable psychophysiological sensors has led to opportunities to measure the mental workload of operators in complex sociotechnical systems in ways that are more objective and less obtrusive. This study primarily focuses on the sensors themselves by investigating low-cost and wearable sensors in terms of their accuracy, obtrusiveness, and usability for research purposes. Two sensors were assessed on their accuracy as tools to measure mental workload through heart rate variability (HRV): the E3 from Empatica and the emWave Pro from HeartMath. The BioPatch from Zephyr Technology, which is an U.S. Food and Drug Administration-approved device, was used as a gold standard to compare the data obtained from the other 2 devices regarding their accuracy on HRV. Linear dependencies for 6 of 10 HRV parameters were found between the emWave and BioPatch data and for 1 of 10 for the E3 sensor. In terms of research usability, both the E3 and the BioPatch had difficulty acquiring either sufficiently high data recording confidence values or normal distributions. However, the BioPatch output files do not require postprocessing, which reduces costs and effort in the analysis stage. None of the sensors was perceived as obtrusive by the participants.

Resilient Practices in Maintaining Safety of Health Information Technologies

Electronic health record systems (EHRs) can improve safety and reliability of health care, but they can also introduce new vulnerabilities by failing to accommodate changes within a dynamic EHR-enabled health care system. Continuous assessment and improvement is thus essential for achieving resilience in EHR-enabled health care systems. Given the rapid adoption of EHRs by many organizations that are still early in their experiences with EHR safety, it is important to understand practices for maintaining resilience used by organizations with a track record of success in EHR use. We conducted interviews about safety practices with 56 key informants (including information technology managers, chief medical information officers, physicians, and patient safety officers) at two large health care systems recognized as leaders in EHR use. We identified 156 references to resilience-related practices from 41 informants. Framework analysis generated five categories of resilient practices: (a) sensitivity to dynamics and interdependencies affecting risks, (b) basic monitoring and responding practices, (c) management of practices and resources for monitoring and responding, (d) sensitivity to risks beyond the horizon, and (e) reflecting on risks with the safety and quality control process itself. The categories reflect three functions that facilitate resilience: reflection, transcending boundaries, and involving sharp-end practitioners in safety management.

Training needs of clinical research associates

Clinical research is a relatively new field in our country that has seen very rapid growth in the last few years. Availability of personnel appropriately trained to the specific requirements of the role they will perform in clinical research is critical for capacity expansion. Our study attempts to understand the specific areas of knowledge and skills that are important for the role of a clinical research associate. The survey was conducted among clinical research professionals from industry and academia who had more than five years of clinical research experience and held important decision making positions in clinical research (stakeholders). The survey questionnaire was designed as a matrix of various clinical research roles on the y-axis and six knowledge modules and eight skills on the x-axis. Respondents were asked to rate the importance of the knowledge /skills to the role of clinical research associates on a three point scale. In discussing results, a significant response was considered to be 50% or greater positive response from the total group. The significant findings were that general, ethics and clinical trial execution modules were rated as critical for the role of clinical research associate. Regulatory module was rated as important for the role. The other significant responses were that three of the sub-topics in the methodology module - framing a research proposal/protocol and experimental design, designing case report forms and EDCs and conducting PK studies - were rated as important and one sub topic in the data management and statistics module was rated as not important. All the skills except leadership skills were rated as critical for the role. The findings of our survey were in general on the lines of expectations of performance of the role. The general, ethics and clinical trial execution modules are critical knowledge areas for the role of a clinical research associate. No clear trends emerged for some of the other modules. Leadership skills were not rated as critical to the role. This kind of a survey gives a good direction when training curriculum has to be designed for specific roles in clinical research. However, there is a need to expand the sample size to fine-tune the knowledge and skills areas.

An analysis of topics and vocabulary in Chinese oral narratives by normal speakers and speakers with fluent aphasia

This study analysed the topic and vocabulary of Chinese speakers based on language samples of personal recounts in a large spoken Chinese database recently made available in the public domain, i.e. Cantonese AphasiaBank ( http://www.speech.hku.hk/caphbank/search/ ). The goal of the analysis is to offer clinicians a rich source for selecting ecologically valid training materials for rehabilitating Chinese-speaking people with aphasia (PWA) in the design and planning of culturally and linguistically appropriate treatments. Discourse production of 65 Chinese-speaking PWA of fluent types (henceforth, PWFA) and their non-aphasic controls narrating an important event in their life were extracted from Cantonese AphasiaBank. Analyses of topics and vocabularies in terms of part-of-speech, word frequency, lexical semantics, and diversity were conducted. There was significant overlap in topics between the two groups. While the vocabulary was larger for controls than that of PWFA as expected, they were similar in distribution across parts-of-speech, frequency of occurrence, and the ratio of concrete to abstract items in major open word classes. Moreover, proportionately more different verbs than nouns were employed at the individual level for both speaker groups. The findings provide important implications for guiding directions of aphasia rehabilitation not only of fluent but also non-fluent Chinese aphasic speakers.

Extracting Hot spots of Topics from Time Stamped Documents

Identifying time periods with a burst of activities related to a topic has been an important problem in analyzing time-stamped documents. In this paper, we propose an approach to extract a hot spot of a given topic in a time-stamped document set. Topics can be basic, containing a simple list of keywords, or complex. Logical relationships such as and, or, and not are used to build complex topics from basic topics. A concept of presence measure of a topic based on fuzzy set theory is introduced to compute the amount of information related to the topic in the document set. Each interval in the time period of the document set is associated with a numeric value which we call the discrepancy score. A high discrepancy score indicates that the documents in the time interval are more focused on the topic than those outside of the time interval. A hot spot of a given topic is defined as a time interval with the highest discrepancy score. We first describe a naive implementation for extracting hot spots. We then construct an algorithm called EHE (Efficient Hot Spot Extraction) using several efficient strategies to improve performance. We also introduce the notion of a topic DAG to facilitate an efficient computation of presence measures of complex topics. The proposed approach is illustrated by several experiments on a subset of the TDT-Pilot Corpus and DBLP conference data set. The experiments show that the proposed EHE algorithm significantly outperforms the naive one, and the extracted hot spots of given topics are meaningful.

A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook

OBJECTIVES

To analyze the content of publications within the medical Natural Language Processing (NLP) domain in 2018.

METHODS

Automatic and manual pre-selection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

RESULTS

Two best papers have been selected this year. One dedicated to the generation of multi- documents summaries and another dedicated to the generation of imaging reports. We also proposed an analysis of the content of main research trends of NLP publications in 2018.

CONCLUSIONS

The year 2018 is very rich with regard to NLP issues and topics addressed. It shows the will of researchers to go towards robust and reproducible results. Researchers also prove to be creative for original issues and approaches.

Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media

BACKGROUND

Long COVID-a condition with persistent symptoms post COVID-19 infection-is the first illness arising from social media. In France, the French hashtag #ApresJ20 described symptoms persisting longer than 20 days after contracting COVID-19. Faced with a lack of recognition from medical and official entities, patients formed communities on social media and described their symptoms as long-lasting, fluctuating, and multisystemic. While many studies on long COVID relied on traditional research methods with lengthy processes, social media offers a foundation for large-scale studies with a fast-flowing outburst of data.

OBJECTIVE

We aimed to identify and analyze Long Haulers' main reported symptoms, symptom co-occurrences, topics of discussion, difficulties encountered, and patient profiles.

METHODS

Data were extracted based on a list of pertinent keywords from public sites (eg, Twitter) and health-related forums (eg, Doctissimo). Reported symptoms were identified via the MedDRA dictionary, displayed per the volume of posts mentioning them, and aggregated at the user level. Associations were assessed by computing co-occurrences in users' messages, as pairs of preferred terms. Discussion topics were analyzed using the Biterm Topic Modeling; difficulties and unmet needs were explored manually. To identify patient profiles in relation to their symptoms, each preferred term's total was used to create user-level hierarchal clusters.

RESULTS

Between January 1, 2020, and August 10, 2021, overall, 15,364 messages were identified as originating from 6494 patients of long COVID or their caregivers. Our analyses revealed 3 major symptom co-occurrences: asthenia-dyspnea (102/289, 35.3%), asthenia-anxiety (65/289, 22.5%), and asthenia-headaches (50/289, 17.3%). The main reported difficulties were symptom management (150/424, 35.4% of messages), psychological impact (64/424,15.1%), significant pain (51/424, 12.0%), deterioration in general well-being (52/424, 12.3%), and impact on daily and professional life (40/424, 9.4% and 34/424, 8.0% of messages, respectively). We identified 3 profiles of patients in relation to their symptoms: profile A (n=406 patients) reported exclusively an asthenia symptom; profile B (n=129) expressed anxiety (n=129, 100%), asthenia (n=28, 21.7%), dyspnea (n=15, 11.6%), and ageusia (n=3, 2.3%); and profile C (n=141) described dyspnea (n=141, 100%), and asthenia (n=45, 31.9%). Approximately 49.1% of users (79/161) continued expressing symptoms after more than 3 months post infection, and 20.5% (33/161) after 1 year.

CONCLUSIONS

Long COVID is a lingering condition that affects people worldwide, physically and psychologically. It impacts Long Haulers' quality of life, everyday tasks, and professional activities. Social media played an undeniable role in raising and delivering Long Haulers' voices and can potentially rapidly provide large volumes of valuable patient-reported information. Since long COVID was a self-titled condition by patients themselves via social media, it is imperative to continuously include their perspectives in related research. Our results can help design patient-centric instruments to be further used in clinical practice to better capture meaningful dimensions of long COVID.

Exploring the Framing of Animal Farming and Meat Consumption: On the Diversity of Topics Used and Qualitative Patterns in Selected Demographic Contexts

Simple Summary

In various contexts, people talk about the farming and consumption of animals using different arguments to construct and justify their (non-)acceptability. This paper reports on a qualitative research among consumers with different backgrounds in urban and rural areas in The Netherlands and Turkey. We present an elaborate methodology for qualitatively researching everyday-life talk about animal farming and meat consumption. We explain how we collected and organised topics people refer to, and looked at the possible relation of complete argumentations with the researched contexts. The resulting long list of topics includes animal welfare arguments, but shows that in everyday-life many others are used, such as health, taste, money, religion, and environmental impact. Our research indicates several ties between mentioned topics and the researched contexts—the most noticeable pattern being the difference between respondents in cities and rural areas. However, in contrast to what literature suggests, single contextual features, like country or gender, offered relatively little insight into the differences that showed up in the complete argumentations. This, we argue, does not imply that context does not matter, but rather that so many cultural and personal contextual aspects play a role that singular contextual features cannot sufficiently explain framing.

Abstract

In various contexts, people talk about animal farming and meat consumption using different arguments to construct and justify their (non-)acceptability. This article presents the results of an in-depth qualitative inquiry into the content of and contextual patterns in the everyday-life framing regarding this issue, performed among consumers in various settings in two extremes in the European sphere: the Netherlands and Turkey. We describe the methodological steps of collecting, coding, and organizing the variety of encountered framing topics, as well as our search for symbolic convergence in groups of consumers from different selected demographic contexts (country, urban-rural areas, gender, age, and education level). The framing of animal farming and meat consumption in everyday-life is not a simple one-issue rational display of facts; people referred to a vast range of topics in the categories knowledge, convictions, pronounced behaviour, values, norms, interests, and feelings. Looking at framing in relation to the researched demographic contexts, most patterns were found on the level of topics; symbolic convergence in lines of reasoning and composite framing was less prominent in groups based on single demographic contexts than anticipated. An explanation for this lies in the complexity of frame construction, happening in relation with multiple interdependent contextual features.

Trends analysis on research articles in the korean journal of medical education

The purpose of this study was to examine the chronological changes and progress in medical education research in Korea and to identify the less investigated topics that need further study and improvement with regard to methodological quality. Of the 590 articles that were published from 1989 to 2010 in the Korean Journal of Medical Education, 386 original research papers were extracted for the analysis. The extracted papers were systematically reviewed using 2 analysis schemes that we developed: one scheme was designed to classify research topics, and the other determined the methodology that was used. The main results were as follows: The most popular research areas were curriculum, educational method, and evaluation in basic medical education; in contrast, studies that addressed postgraduate education, continuous professional development, and educational administration were less frequent; The most frequently studied topics were clinical performance/skills evaluation, clerkship, curriculum development, and problem-based learning, Quantitative studies predominated over qualitative studies and mixed methods (265 vs. 95 vs. 26). Two hundred forty papers were descriptive, cross-sectional studies, and 17 were experimental studies. Most qualitative studies were non-participation observational studies. In conclusion, there has been dramatic growth in the extent of medical education research in Korea in the past two decades. However, more studies that investigate the graduate medical education and the continuous professional development should be performed. Moreover, robust experimental designs and methods should be applied to provide stronger evidence that can practice best-evidence medical education.

Editorial: MTTP, hepatic steatosis and plasma lipids - still more questions than answers

This article is linked to Schneider et al papers. To view these articles, visit https://doi.org/10.1111/apt.17566 and https://doi.org/10.1111/apt.17624

Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area

OBJECTIVES

To analyse the content of publications within the medical Natural Language Processing (NLP) domain in 2022.

METHODS

Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

RESULTS

Three best papers have been selected. We also propose an analysis of the content of the NLP publications in 2022, stressing on some of the topics.

CONCLUSION

The main trend in 2022 is certainly related to the availability of large language models, especially those based on Transformers, and to their use by non-NLP researchers. This leads to the democratization of the NLP methods. We also observe the renewal of interest to languages other than English, the continuation of research on information extraction and prediction, the massive use of data from social media, and the consideration of needs and interests of patients.

Psychosocial counselling for intended parents who opt for donor sperm treatment: which topics do they find relevant?

Objective: This study aimed to explore which topics intended parents who opt for donor sperm treatment find relevant to discuss in psychosocial counselling. Background: The choice for donor sperm treatment has psychosocial implications for intended parents and therefore psychosocial counselling is advised as an integral part of DST. To date, little is known about which topics intended parents find relevant to discuss in psychosocial counselling. Methods: We conducted 25 semi-structured in-depth interviews between 2015 and 2017 with heterosexual men and women, lesbian women and single women who opted for donor sperm treatment and had a counselling session as part of their intake. They were recruited through three Dutch fertility centres, three network organisations and by snowball sampling. Results: Intended parents found it relevant to discuss the following seven topics in psychosocial counselling: the decision to opt for donor sperm treatment, choosing a sperm donor, coping with questions from family and friends, non-genetic parenthood, single motherhood, openness and disclosure, and future contact between the child and half-siblings. Conclusion: We recommend that counsellors take a more active role in bringing up the topics found in our study and that a clear distinction is made between counselling with the aim to screen intended parents and counselling with the aim to offer guidance.

A text mining and network analysis of topics and trends in major nursing research journals

AIM

This study is set to determine the main topics of the nursing field and to show the changing perspectives over time by analysing the abstracts of several major nursing research journals using text mining methodology.

DESIGN

Text mining and network analysis.

METHODS

Text analysis combines automatic and manual operations to identify patterns in unstructured data. Detailed searches covering 1998-2021 were conducted in PubMed archives to collect articles from six nursing journals: Journal of Advanced Nursing, International Journal of Nursing Studies, Western Journal of Nursing Research, Nursing Research, Journal of Nursing Scholarship and Research in Nursing and Health. This study uses a four-phase text mining and network approach, gathering text data and cleaning, preprocessing, text analysis and advanced analyses. Analyses and data visualization were performed using Endnote, JMP, Microsoft Excel, Tableau and VOSviewer versions. From six journals, 17,581 references in PubMed were combined into one EndNote file. Due to missing abstract information, 2496 references were excluded from the study. The remaining references (n = 15,085) were used for the text mining analyses.

RESULTS

Eighteen subjects were determined into two main groups; research method topics and nursing research topics. The most striking topics are qualitative research, concept analysis, advanced practice in the downtrend, and literature search, statistical analysis, randomized control trials, quantitative research, nurse practice environment, risk assessment and nursing science. According to the network analysis results, nursing satisfaction and burnout and nursing practice environment are highly correlated and represent 10% of the total corpus. This study contributes in various ways to the field of nursing research enhanced by text mining. The study findings shed light on researchers becoming more aware of the latest research status, sub-fields and trends over the years, identifying gaps and planning future research agendas. No patient or public contribution.

Research hotspots and trends for Duchenne muscular dystrophy: a machine learning bibliometric analysis from 2004 to 2023

Aims

The aim of this study was to conduct a bibliometric analysis of the relevant literature on Duchenne muscular dystrophy (DMD) to ascertain its current status, identify key areas of research and demonstrate the evolution of the field.

Methods

The analysis sourced documents from the Science Citation Index Expanded in the Web of Science core collection, utilizing CiteSpace software and an online bibliometric platform to analyze collaborative networks among authors, institutions and countries, and to map out the research landscape through journal and reference evaluations. Keyword analyses, including clustering and emergent term identification, were conducted, alongside the development of knowledge maps.

Results

The study included 9,277 documents, indicating a rising publication trend in the field. The Institut National de la Santé et de la Recherche Médicale emerged as the top publishing institution, with Francesco Muntoni as the most prolific author. The United States dominated in publication output, showcasing significant leadership. The keyword analysis highlighted 786 key emergent terms, primarily focusing on the mechanisms, diagnostics and treatment approaches in DMD.

Conclusion

The field of DMD research is experiencing robust growth, drawing keen interest globally. A thorough analysis of current research and trends is essential for advancing knowledge and therapeutic strategies in this domain.

Editorial: clinical outcomes in lean NAFLD - the devil is in the details. Authors' reply

This article is linked to Wijarnpreecha et al papers. To view these articles, visit https://doi.org/10.1111/apt.17424 and https://doi.org/10.1111/apt.17449

Panel composition and disparities in the topics of presentation in vascular surgery conferences

BACKGROUND

Previous studies identified gender disparities in surgical conference presenters and moderators. We sought to assess disparities in the representation of women in terms of speakers and moderators, but with particular emphasis on panels and topics of discussion at vascular surgery conferences.

METHODS

Data regarding presenters and moderators from the Southern Association of Vascular Surgery, Western Vascular Society, Vascular and Endovascular Surgical Society, Society for Clinical Vascular Surgery, and Society for Vascular Surgery conferences was obtained from online meeting archives and via email correspondence. Scientific session speakers, moderators, and panelists were identified by sex. Specific vascular topics for each discussion were also identified. Keynote speakers or special guests were excluded.

RESULTS

Compared to men, women were less often presenters (18% versus 82%, p < .002) and moderators (16% versus 84%, p < .001) of conference sessions. Women were most likely to present on dialysis access and least likely to present on venous disease overall. Women were more likely to present on aortic (24% vs 19%; p < .013) and cerebrovascular disease (33% vs 27%; p < .021) at regional compared to national conferences. Of panels assessed, 68% were all-male. Subgroup analysis suggests that some improvements have been made over time.

CONCLUSIONS

Significant disparities persist in the topics presented and in panel composition suggesting potential areas for improvement in equity. Further study should focus on evaluating trends in the training level of the presenter and the topics presented, and assessing parity in structural factors that impact research presentation opportunities.

Year 2021: COVID-19, Information Extraction and BERTization among the Hottest Topics in Medical Natural Language Processing

OBJECTIVES

Analyze the content of publications within the medical natural language processing (NLP) domain in 2021.

METHODS

Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

RESULTS

Four best papers have been selected in 2021. We also propose an analysis of the content of the NLP publications in 2021, all topics included.

CONCLUSIONS

The main issues addressed in 2021 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as information extraction and use of information from social networks.

Assessing the Topics and Motivating Factors Behind Human-Social Chatbot Interactions: Thematic Analysis of User Experiences

Background

Although social chatbot usage is expected to increase as language models and artificial intelligence improve, very little is known about the dynamics of human-social chatbot interactions. Specifically, there is a paucity of research examining why human-social chatbot interactions are initiated and the topics that are discussed.

Objective

We sought to identify the motivating factors behind initiating contact with Replika, a popular social chatbot, and the topics discussed in these interactions.

Methods

A sample of Replika users completed a survey that included open-ended questions pertaining to the reasons why they initiated contact with Replika and the topics they typically discuss. Thematic analyses were then used to extract themes and subthemes regarding the motivational factors behind Replika use and the types of discussions that take place in conversations with Replika.

Results

Users initiated contact with Replika out of interest, in search of social support, and to cope with mental and physical health conditions. Users engaged in a wide variety of discussion topics with their Replika, including intellectual topics, life and work, recreation, mental health, connection, Replika, current events, and other people.

Conclusions

Given the wide range of motivational factors and discussion topics that were reported, our results imply that multifaceted support can be provided by a single social chatbot. While previous research already established that social chatbots can effectively help address mental and physical health issues, these capabilities have been dispersed across several different social chatbots instead of deriving from a single one. Our results also highlight a motivating factor of human-social chatbot usage that has received less attention than other motivating factors: interest. Users most frequently reported using Replika out of interest and sought to explore its capabilities and learn more about artificial intelligence. Thus, while developers and researchers study human-social chatbot interactions with the efficacy of the social chatbot and its targeted user base in mind, it is equally important to consider how its usage can shape public perceptions and support for social chatbots and artificial agents in general.