Applications and Uses of Dental Ontologies

The development of a number of large-scale semantically-rich ontologies for biomedicine attests to the interest of life science researchers and clinicians in Semantic Web technologies. To date, however, the dental profession has lagged behind other areas of biomedicine in developing a commonly accepted, standardized ontology to support the representation of dental knowledge and information. This paper attempts to identify some of the potential uses of dental ontologies as part of an effort to motivate the development of ontologies for the dental domain. The identified uses of dental ontologies include support for advanced data analysis and knowledge discovery capabilities, the implementation of novel education and training technologies, the development of information exchange and interoperability solutions, the better integration of scientific and clinical evidence into clinical decision-making, and the development of better clinical decision support systems. Some of the social issues raised by these uses include the ethics of using patient data without consent, the role played by ontologies in enforcing compliance with regulatory criteria and legislative constraints, and the extent to which the advent of the Semantic Web introduces new training requirements for dental students. Some of the technological issues relate to the need to extract information from a variety of resources (for example, natural language texts), the need to automatically annotate information resources with ontology elements, and the need to establish mappings between a variety of existing dental terminologies

Social representations of HIV/AIDS in five Central European and Eastern European countries: A multidimensional analysis

Cognitive processing models of risky sexual behaviour have proliferated in the two decades since the first reporting of HIV/AIDS, but far less attention has been paid to individual and group representations of the epidemic and the relationship between these representations and reported sexual behaviours. In this study, 494 business people and medics from Estonia, Georgia, Hungary, Poland and Russia sorted free associations around HIV/AIDS in a matrix completion task. Exploratory factor and multidimensional scaling analyses revealed two main dimensions (labelled ‘Sex’ and ‘Deadly disease’), with significant cultural and gender variations along both dimension scores. Possible explanations for these results are discussed in the light of growing concerns over the spread of the epidemic in this region

Challenges experienced by women high performance coaches and its association with sustainability in the profession

High performance (HP) coaching is a demanding profession (Didymus, 2017). The proportion of woman HP coaches is reported to be in the range of 8.4% - 20% (Bentzen, Lemyre, & Kenttä, 2016a; Kidd, 2013). Mental health concerns in elite sports have recently gained attention, but mainly focusing on athletes (Henriksen et al., 2019). Beyond coach burnout, limited attention has been given to coaches’ mental health. A recent coach burnout review (Olusoga, Bentzen, & Kenttä, 2019) included only one paper that focused exclusively on women. It has been argued that women HP coaches face greater challenges in a male-dominated coaching culture. The purpose of this study was to explore challenges experienced by women HP coaches and their perceived associations with sustainability and mental health. Thirty-seven female HP coaches participated by answering a semi-structured, open-ended questionnaire. All responses were analyzed using inductive thematic analysis, which resulted in two general dimensions: Challenges of Working as a WHPC and Sustainability and Well-being as a WHPC. Overall, results indicate that challenges reported might be common for all HP coaches, but also highlight gender specific elements. Consequently, coach retention and sustainability would benefit from more attention on well-being and mental health among HP coaches

SurveyMan: Programming and Automatically Debugging Surveys

Surveys can be viewed as programs, complete with logic, control flow, and bugs. Word choice or the order in which questions are asked can unintentionally bias responses. Vague, confusing, or intrusive questions can cause respondents to abandon a survey. Surveys can also have runtime errors: inattentive respondents can taint results. This effect is especially problematic when deploying surveys in uncontrolled settings, such as on the web or via crowdsourcing platforms. Because the results of surveys drive business decisions and inform scientific conclusions, it is crucial to make sure they are correct. We present SurveyMan, a system for designing, deploying, and automatically debugging surveys. Survey authors write their surveys in a lightweight domain-specific language aimed at end users. SurveyMan statically analyzes the survey to provide feedback to survey authors before deployment. It then compiles the survey into JavaScript and deploys it either to the web or a crowdsourcing platform. SurveyMan's dynamic analyses automatically find survey bugs, and control for the quality of responses. We evaluate SurveyMan's algorithms analytically and empirically, demonstrating its effectiveness with case studies of social science surveys conducted via Amazon's Mechanical Turk.Comment: Submitted version; accepted to OOPSLA 201

Making Study Populations Visible through Knowledge Graphs

Treatment recommendations within Clinical Practice Guidelines (CPGs) are largely based on findings from clinical trials and case studies, referred to here as research studies, that are often based on highly selective clinical populations, referred to here as study cohorts. When medical practitioners apply CPG recommendations, they need to understand how well their patient population matches the characteristics of those in the study cohort, and thus are confronted with the challenges of locating the study cohort information and making an analytic comparison. To address these challenges, we develop an ontology-enabled prototype system, which exposes the population descriptions in research studies in a declarative manner, with the ultimate goal of allowing medical practitioners to better understand the applicability and generalizability of treatment recommendations. We build a Study Cohort Ontology (SCO) to encode the vocabulary of study population descriptions, that are often reported in the first table in the published work, thus they are often referred to as Table 1. We leverage the well-used Semanticscience Integrated Ontology (SIO) for defining property associations between classes. Further, we model the key components of Table 1s, i.e., collections of study subjects, subject characteristics, and statistical measures in RDF knowledge graphs. We design scenarios for medical practitioners to perform population analysis, and generate cohort similarity visualizations to determine the applicability of a study population to the clinical population of interest. Our semantic approach to make study populations visible, by standardized representations of Table 1s, allows users to quickly derive clinically relevant inferences about study populations.Comment: 16 pages, 4 figures, 1 table, accepted to the ISWC 2019 Resources Track (https://iswc2019.semanticweb.org/call-for-resources-track-papers/

Utility of an Emotion Coding System for Parent-Child Interaction Therapy with Toddlers

Numerous efficacious early interventions target and alter caregiver-child interactions to promote optimal social-emotional outcomes for young children (Bagner et al., 2014). However, research has primarily relied on the use of caregiver report to assess caregiver-child emotion-focused practices, revealing the need for a behavioral observation assessment (Zinsser et al., 2021). Preliminary evidence suggests that Parent-Child Interaction Therapy with Toddlers (PCIT-T) is a well-received and efficacious intervention for reducing disruptive behaviors, improving child internalizing and externalizing behavior, reducing parental stress, and increasing parental sensitivity (Kohlhoff et al., 2021; Kohlhoff, Cibralic, & Morgan, 2020). PCIT-T strives to train caregivers to interact with their toddlers in a nurturing and sensitive manner to promote healthy attachment, improve child emotion regulation skills, and enhance child emotion socialization. Presently, PCIT-T lacks a well-established observational emotion coding system that would benefit treatment and the broader field of clinical child psychology in measuring outcomes in caregiver-child emotion-focused practices. The Dyadic Emotion Coding System (DECS) was developed to measure caregiver-toddler emotion talk emotion-focused practices. The current study evaluated the validity, reliability, and clinical utility of the DECS with archival data extracted from a randomized clinical trial of PCIT-T with 90 caregiver-toddler dyads referred for treatment of child behavior problems. DECS codes were significantly associated with maternal sensitivity, as well as exploratory relationships with caregiver and child emotion regulation. After undergoing PCIT-T, caregivers significantly improved in their use of adaptive emotion-focused practices. Practical utility of a standardized DECS training procedure was demonstrated via test-retest reliability (κ = .78). Evidence suggests the DECS would provide a well-established observational emotion coding system to benefit PCIT-T