Service-Learning: A Venue for Enhancing Pre-ServiceEducators’ Knowledge Base for Teaching

Scholarship of Teaching and Learning research examining the impact of service-learning on student’s personal qualities has shown positive results. Findings indicate that students participating in high quality service-learning programs show increases in their perceptions of self-efficacy, civic responsibility, social justice, and diversity awareness. Less information regarding the effect of participation in service-learning on student’s intellectual and knowledge outcomes is known. This case study examined the influence of participation in a service-learning program on pre-service educators’ knowledge base for teaching. Participants included 31 undergraduate physical education majors enrolled in a Motor Skill Development for Children course at a large state university in the southwestern United States. Findings from multiple data sources (i.e., journals, interviews, and observations of instruction) revealed that pre-service educators participating in a service-learning program enhanced their pedagogical content knowledge. Teacher education programs should enhanced their pedagogical content knowledge. Teacher education programs should enhanced their pedagogical content knowledge. Teacher education programs should consider implementing service-learning programs within the curriculum to benefit pre-service educators’ knowledge base for teaching

Student and Faculty Perception of Engagement in Two Active Learning Classroom Designs

Faculty and student perception of engagement in two (mobile desks or mobile tables and chairs) low-tech active learning classroom (ALC) designs were compared. Student (n = 413) perceptions of engagement were measured with the Engaged Learning Index (ELI) and the Social Context and Learning Environments (SCALE) instruments at the beginning and end of a semester in a large, multi-disciplinary department. Faculty (n = 14) rated perception of engagement using only the SCALE instrument. Perceptions of engagement from faculty and students using SCALE were significantly more positive for both ALCs compared to perceptions of traditional classrooms. There was no clear evidence of differences in student and faculty perceptions of engagement between the two ALC designs. No or small differences between the two ALC designs means departments might consider cost, maintenance, and other pragmatic factors in ALC design

Active Learning Training and Classroom Renovation: Exploring Student and Faculty Perceptions in Health and Human Performance Disciplines

Active learning spaces form an important part of university learning environments and have the potential to enhance student learning, yet studies on student and faculty perceptions of collaborative learning pedagogies indicate many remain resistant. To overcome this resistance, an academic department developed and implemented an active learning initiative to assist faculty transiting to teach in a classroom newly renovated for active learning pedagogies. Five semi-structured focus groups explored perceptions of faculty and students in the inaugural classes in the renovated space to identify what they perceived enhanced or detracted from faculty delivery of content and student learning experiences. Thematic analysis revealed three themes: Positive improvements in the physical classroom environment, enhanced student engagement, and improved instructional methodology because of faulty training and classroom renovation. Key findings indicated primarily positive perceptions of the renovated physical environment, especially the tables and mobile white boards; however, participants also noted some frustrations with the furniture, classroom layout, and technology influencing student engagement and effectiveness of active learning strategies. Overall, data supported the conclusion that the classroom renovation and faculty training program effectively facilitated positive learning experiences and student-instructor interactions

Active learning classroom design and student engagement: An exploratory study

Three student engagement measures were collected for a class taught by an experienced instructor in two active learning classrooms with dissimilar seating arrangements. Student perception of engagement was similar between the learning spaces. However, instructor perception and researcher observation indicated greater engagement in the classroom with mobile tables compared to the classroom with mobile desks. STROBE classroom observations indicated qualitatively different student-to-student (8% greater), student-to-instructor (3% greater), and student self- (6.5% less) engagement in the mobile table classroom over the mobile desks classroom. Instructor and student perceptions may interact to affect student engagement with various designs of active learning classrooms

Intrapartum fetal death and doctors; A qualitative exploration

Introduction: The death of an infant during a pregnancy is profoundly traumatic, both for the parents and the involved healthcare professionals. Most research focuses on the impact of antenatal stillbirth with very little research examining the specific impact an intrapartum fetal death has on obstetricians. The aim of this study was to provide an in‐depth qualitative exploration of the attitudes and responses that Irish Obstetricians have following direct involvement with an intrapartum fetal death. Material and methods: Qualitative semi‐ structured interviews were used. Interpretative phenomenology was used for data analysis. The setting was a tertiary university maternity unit in Ireland with 8200 deliveries per year. Ten obstetricians were purposively sampled. The main outcome measures were the attitudes and responses of Irish obstetricians following exposure to an intrapartum death. Results: Obstetricians were profoundly and negatively affected by a personal involvement with an intrapartum death. Analysis of the data revealed two superordinate themes; the doctor as a person, and supporting each other. The doctor as person was characterised by two subordinate themes; emotional impact and frustration. Supporting each other was also characterised by two subordinate themes; an unmet need and incidental support and what might work. Conclusions: Obstetric doctors who are directly involved in an intrapartum death are the second victims of this event and this is something that needs to be acknowledged; by the public, by the healthcare system, by the media and by the doctors themselves. The development of effective emotional support interventions for all obstetricians is highly important

Examining Service-Learning in a Graduate Physical Education Teacher Education Course

This study was designed to explore the impact of service-learning on graduate physical education teacher education students. Social-Cognitive Theory (Bandura, 1986; 1999) served as the framework to examine graduate student’s experiences in a service-learning program. Participants were graduate students (N =16) enrolled in a curriculum and instruction in physical education course at a major university in the southwest United States. The course’s service-learning component provided graduate students opportunities to teach physical activity to Hispanic-American and African-American children from low-socioeconomic backgrounds. Participant’s described their experiences through weekly reflections and discussions. Content analysis of data sources indicated that participation in the service-learning program strengthened graduate student’s efficacy for teaching, contributed to their acquisition of varied teaching strategies, and enhanced graduate students understanding of children living in low-income, minority households. Findings suggest service-learning can be a valuable pedagogy to infuse into graduate teacher education programs

Using Biomedical Text as Data and Representation Learning for Identifying Patients with an Osteoarthritis Phenotype in the Electronic Medical Record

Introduction Electronic medical records (EMRs) are increasingly used in health services research. Accurate/efficient identification of a target population with a specific disease phenotype is a necessary precursor to studying the health of these individuals. Objectives and Approach We explored the use of biomedical text as inputs to supervised phenotype identification algorithms. We employed a two-stage classification approach to map the discrete, sparse high-dimensional biomedical text data to a dense low dimensional vector space using methods from unsupervised machine learning. Next we used these learned vectors as inputs to supervised machine learning algorithms for phenotype identification. We were able to demonstrate the applicability of the approach to identifying patients with an osteoarthritis (OA) phenotype using primary care data from the Electronic Medical Record Administrative data Linked Database (EMRALD) held at ICES. Results EMRALD contains approximately 20Gb of biomedical text data on approximately 500,000 patients. The unit of analysis for this study is the patient. We were interested in identifying OA patients using solely text data as features. Labelled outcome information wass available from a random sample of 7,500 patients. We divided patients into training (N=6000), validation (N=750) and test (N=750) cohorts. We learned low dimensional representations of the input text data on the entire EMRALD corpus (N=500,000). We used learned numeric vectors as inputs to supervised machine learning models for OA classification (N=6,000 training set patients). We compared models in terms of accuracy, sensitivity, specificity, PPV and NPV. The best learned models achieved approximately 90\% sensitivity and 80\% specificity. Classification accuracy varied as a function of learned inputs. Conclusion/Implications We developed an approach to phenotype identification using solely biomedical text as an input. Preliminary results suggest our two-stage ML approach has improved operating characteristics compared to existing clinically derived decision rules for OA classification. Future work will explore the generalizability of this methodology to other disease phenotypes

Learning Unsupervised Representations from Biomedical Text

Introduction Healthcare settings are becoming increasingly technological. Interactions/events involving healthcare providers and the patients they service are captured as digital text. Healthcare organizations are amassing increasingly large/complex collections of biomedical text data. Researchers and policy makers are beginning to explore these text data holdings for structure, patterns, and meaning. Objectives and Approach EMRALD is a primary care electronic medical record (EMR) database, comprised of over 40 family medicine clinics, nearly 400 primary care physicians and over 500,000 patients. EMRALD includes full-chart extractions, including all clinical narrative information/data in a variety of fields. The input data (raw text strings) are discrete, sparse and high dimensional. We assessed scalable statistical models for high dimensional discrete data, including fitting, assessing and exploring models from three broad statistical areas: i) matrix factorization/decomposition models ii) probabilistic topic models and iii) word-vector embedding models. Results EMRALD is comprised of 12 text data streams. EMRALD text data is structured into 84 million clinical notes (3.5 billion word/language tokens) and is approximately 18Gb in storage size. We employ a “text as data” pipeline, i) mapping raw strings to sequences of word/language tokens, ii) mapping token sequences to numeric arrays, and finally iii) using numeric arrays as inputs to statistical models. Fitted topic models yield useful thematic summaries of the EMRALD corpora. Topics discovered reflect core responsibilities of primary care physicians (e.g. women’s health, pain management, nutrition/diet, etc.). Fitted vector embedding models capture structure of discourse/syntax. Related words are mapped to similar locations of vector spaces. Analogical reasoning is possible in the embedding space. Conclusion/Implications “Text as data” requires an understanding of statistical models for discrete, sparse, high dimensional data. We fit a variety of unsupervised statistical models to biomedical text data. Preliminary results suggest that the learned low dimensional representations of the biomedical text data are effective at uncovering meaningful patterns/structure

Variability in newborn telomere length is explained by inheritance and intrauterine environment

Background: Telomere length (TL) and its attrition are important indicators of physiological stress and biological aging and hence may vary among individuals of the same age. This variation is apparent even in newborns, suggesting potential effects of parental factors and the intrauterine environment on TL of the growing fetus. Methods: Average relative TLs of newborns (cord tissue, N = 950) and mothers (buffy coat collected at 26-28 weeks of gestation, N = 892) were measured in a birth cohort. This study provides a comprehensive analysis of the effects of heritable factors, socioeconomic status, and in utero exposures linked with maternal nutrition, cardiometabolic health, and mental well-being on the newborn TL. The association between maternal TL and antenatal maternal health was also studied. Results: Longer maternal TL (beta = 0.14, P = 1.99E-05) and higher paternal age (beta = 0.10, P = 3.73E-03) were positively associated with newborn TL. Genome-wide association studies on newborn and maternal TLs identified 6 genetic variants in a strong linkage disequilibrium on chromosome 3q26.2 (Tag SNP-LRRC34-rs10936600: P-meta = 5.95E-08). Mothers with higher anxiety scores, elevated fasting blood glucose, lower plasma insulin-like growth factor-binding protein 3 and vitamin B12 levels, and active smoking status during pregnancy showed a higher risk of giving birth to offspring with shorter TL. There were sex-related differences in the factors explaining newborn TL variation. Variation in female newborn TL was best explained by maternal TL, mental health, and plasma vitamin B12 levels, while that in male newborn TL was best explained by paternal age, maternal education, and metabolic health. Mother's TL was associated with her own metabolic health and nutrient status, which may have transgenerational effects on offspring TL. Conclusions: Our findings provide a comprehensive understanding of the heritable and environmental factors and their relative contributions to the initial setting of TL and programing of longevity in early life. This study provides valuable insights for preventing in utero telomere attrition by improving the antenatal health of mothers via targeting the modifiable factors.Peer reviewe