15-An Archaeological Survey in Pavilion and Schoolcraft Townships, Kalamazoo County, Michigan

During the 1984 field season, Dr. William Cremin and the Western Michigan University archaeological field school continued the program of site location survey that had been initiated two years earlier in Pavilion Township (T3S RlOW), Kalamazoo County, Michigan. In addition, a small area flanking the north end of Barton Lake in nearby Schoolcraft Township (T4S RllW) was similarly evaluated. With the cooperation of numerous area landowners and local artifact collectors, almost 40 parcels of land aggregating 361 ha in Pavilion and 33 ha in Schoolcraft townships were surveyed by means of surface reconnaissance procedures. There follows a report of our survey activity, including descriptions of the archaeological sites that were recorded and collected and recommendations regarding the proper disposition of several \u27\u27problem\u27\u27 sites; the latter reflect in one instance disagreement among the documentary sources as to the location of a burial mound and in a second the erroneous recording of a natural feature on the landscape as a cultural phenomenon (i.e. burial mound)

Assessing Parental Self-Efficacy for Obesity Prevention Related Behaviors

Background: Reliable, valid and theoretically consistent measures that assess a parent’s self-efficacy for helping a child with obesity prevention behaviors are lacking. Objectives: To develop measures of parental self-efficacy for four behaviors: 1) helping their child get at least 60 minutes of moderate intensity physical activity every day, 2) helping one’s child consume five servings of fruits and vegetables each day, 3) limiting sugary drinks to once a week, and 4) limiting consumption of fruit juice to 6 ounces every day. Methods: Sequential methods of scale development were used. An item pool was generated based on theory and qualitative interviews, and reviewed by content experts. Scales were administered to parents or legal guardians of children 4–10 years old. The item pool was reduced using principal component analysis. Confirmatory factor analysis tested the resulting models in a separate sample. Subjects: 304 parents, majority were women (88%), low-income (61%) and single parents (61%). Ethnic distribution was 40% Black and 37% white. Results: All scales had excellent fit indices: Comparative fit index \u3e .98 and chi-squares (Pediatrics 120 Suppl 4:S229-253, 2007) = .85 – 7.82. Alphas and one-week test-retest ICC’s were ≥ .80. Significant correlations between self-efficacy scale scores and their corresponding behaviors ranged from .13-.29 (all p \u3c .03). Conclusions: We developed four, four-item self-efficacy scales with excellent psychometric properties and construct validity using diverse samples of parents

Evaluation of a web-based asthma self-management system: a randomised controlled pilot trial

Background Asthma is the most common chronic condition of childhood and disproportionately affects inner-city minority children. Low rates of asthma preventer medication adherence is a major contributor to poor asthma control in these patients. Web-based methods have potential to improve patient knowledge and medication adherence by providing interactive patient education, monitoring of symptoms and medication use, and by facilitation of communication and teamwork among patients and health care providers. Few studies have evaluated web-based asthma support environments using all of these potentially beneficial interventions. The multidimensional website created for this study, BostonBreathes, was designed to intervene on multiple levels, and was evaluated in a pilot trial. Methods An interactive, engaging website for children with asthma was developed to promote adherence to asthma medications, provide a platform for teamwork between caregivers and patients, and to provide primary care providers with up-to-date symptom information and data on medication use. Fifty-eight (58) children primarily from inner city Boston with persistent-level asthma were randomised to either usual care or use of BostonBreathes. Subjects completed asthma education activities, and reported their symptoms and medication use. Primary care providers used a separate interface to monitor their patients’ website use, their reported symptoms and medication use, and were able to communicate online via a discussion board with their patients and with an asthma specialist. Results After 6-months, reported wheezing improved significantly in both intervention and control groups, and there were significant improvements in the intervention group only in night-time awakening and parental loss of sleep, but there were no significant differences between intervention and control groups in these measures. Emergency room or acute visits to a physician for asthma did not significantly change in either group. Among the subgroup of subjects with low controller medication adherence at baseline, adherence improved significantly only in the intervention group. Knowledge of the purpose of controller medicine increased significantly in the intervention group, a statistically significant improvement over the control group. Conclusions This pilot study suggests that a multidimensional web-based educational, monitoring, and communication platform may have positive influences on pediatric patients’ asthma-related knowledge and use of asthma preventer medications

Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach

Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic disease, heart disease and diabetes, and develop data-driven methods to predict hospitalizations due to these conditions. We base these predictions on the patients' medical history, recent and more distant, as described in their Electronic Health Records (EHR). We formulate the prediction problem as a binary classification problem and consider a variety of machine learning methods, including kernelized and sparse Support Vector Machines (SVM), sparse logistic regression, and random forests. To strike a balance between accuracy and interpretability of the prediction, which is important in a medical setting, we propose two novel methods: K-LRT, a likelihood ratio test-based method, and a Joint Clustering and Classification (JCC) method which identifies hidden patient clusters and adapts classifiers to each cluster. We develop theoretical out-of-sample guarantees for the latter method. We validate our algorithms on large datasets from the Boston Medical Center, the largest safety-net hospital system in New England

Pennsylvania Folklife Vol. 31, No. 3

• Jamison City • Domestic Architecture in Lancaster County • Conversation with Marguerite de Angeli • Who Put the Turnip on the Grave? • Pennsylfawnisch Deitsch un Pfalzer: Dialect Comparisons Old and New • John Philip Boehm: Pioneer Pennsylvania Pastor • The Search for our German Ancestors • Aldes un Neieshttps://digitalcommons.ursinus.edu/pafolklifemag/1095/thumbnail.jp