Creating a Monster: Attachment Theory in Mary Shelley’s Frankenstein
Sam Passey, Lyndsey Graig, Christine Fiscer, RonJai Staton, Jeremy Scritchfield, Barbara Balbas, Amy Harmon, Craig Demke, Joey Jergins, Tim Bywater, and Dannelle Larsen Rife, Dixie State University Life Sciences Research in human development suggests relationships are vital for physiological and emotional well-being across the lifespan. Attachment theory is foundational for relationships and is intrinsic in human nature as it is represented through words of novelists. Attachments are developed within the first year of life based on caregivers’ appropriate, contingent, and prompt responses to the infant’s cues. Avoidant attachment develops when the infant receives minimal responses to his or her cues. John Bowlby proposed the attachment relationship between the infant and parent creates an internal working model (IWM). This IWM sets the foundation of all subsequent close relationships throughout the lifespan. Individuals who have avoidant attachment representations are dismissive of, and lack security in relationships. Living in a time where women were marginalized, segregated, and many lacked formal education, Mary Shelley effectively produced a popular work of fiction in the early 1800s. Shelley was a keen observer of relationships long before Attachment Theory was developed in the 1960s. Psychobiographical methods were used to examine Shelley’s Frankenstein as a case study of Attachment Theory. Results suggest Shelley’s Frankenstein depicts basic components of attachment theory, and “Frankenstein,” the monster character, exemplifies avoidant attachment. Through his dismissive and proximity seeking behaviors, the monster characterizes Bowlby’s description of avoidant attachment. Lacking relationships during critical periods for development of empathy, the monster loses the ability to feel remorse. This critical examination of early British literature as a case study for Attachment Theory lends retrospective support for the understanding of human relationships.
A Framework for Validating Modeled Air Quality Data in Health Research
Nicole Burnett, Peter Mo, Naresh Rajan, Randy Madsen, Ram Gouripeddi, and Julio Facelli, University of Utah Life Sciences BackgroundIn the Salt Lake Valley there are three permanent Environmental Protection Agency (EPA) certified air quality monitoring stations that intake air samples and produce results of the air quality in the proximity of the monitor station. Due to the fact that the monitors only represent a small area of the 500- square-mile Salt Lake Valley, there are spatial gaps when using the air quality monitoring data for epidemiological studies. Since for certain studies health researchers may require a higher resolution spatiotemporal air quality grid , we need to devise new approaches to provide air quality data that could meet the epidemiological studies requirements. Research MethodologyModeled air quality data available from the EPA, has higher spatial and temporal resolution than data from monitoring stations, but it needs experimental validation and uncertainty quantification (UQ) in the Salt Lake Valley. We can achieve these validation and UQ goals through statistical comparisons of measured air quality data at the same location and time as the modeled air quality data. The air quality model that we primarily used is the one that the EPA has developed for the Centers for Disease Control and Prevention’s (CDC) National Environmental Public Health Tracking Network . This is a model that uses a Hierarchical Bayesian Space Time Modeling approach . This model was validated on the east coast of the United States so it is unknown how effective is in taking into account the terrain of the Salt Lake Valley. Modeled PM2.5 data in a 12×12 kilometer continuous grid resolution for the years 2007 and 2008 were compared against measured data of the same timeframe and location. The measured data was obtained from EPA’s Air Quality System (AQS) Datamart . The statistical comparisons performed using these the two data sets were done using daily and monthly PM2.5 averages for the years 2007 and 2008 using MySQL, MATLAB and R. Conclusion & SignificanceWe have developed a prototype for comparing and validating modeled air quality data against measured air quality data for the Salt Lake Valley. We found the modeled data fits the measured data fairly well. We will expand our work by developing a validating framework that will include a library of data modeling algorithms such as, The Complex Terrain Dispersion Model Plus Algorithms for Unstable Situations (CTDMPLUS)  and Yanosky’s , which could be selected by the user. The framework will be developed using OpenFurther, and then integrated with biomedical data . The framework will be integrated into the PRISMS project  as part of the informatics infrastructure for studying the effects of air quality on pediatric asthma. ReferencesM. Z. Al-Hamdan, W. L. Crosson, A. S. Limaye, D. L. Rickman, D. A. Quattrochi, M. G. Estes, J. R. Qualters, A. H. Sinclair, D. D. Tolsma, K. A. Adeniyi, and A. S. Niskar, “Methods for Characterizing Fine Particulate Matter Using Ground Observations and Remotely Sensed Data: Potential Use for Environmental Public Health Surveillance,” J. Air Waste Manag. Assoc., vol. 59, no. 7, pp. 865-881, Jul. 2009. “Air Quality Data for the CDC National EPHT Network | Human Exposure and Atmospheric Sciences | US EPA.” [Online]. Available: http://www.epa.gov/heasd/research/cdc.html. [Accessed: 18-Sep-2014]. N. J. McMillan, D. M. Holland, M. Morara, and J. Feng, “Combining numerical model output and particulate data using Bayesian space-time modeling,” Environmetrics, p. n/a-n/a, 2009. “AirData | US Environmental Protection Agency.” [Online]. Available: http://www3.epa.gov/airdata/index.html. [Accessed: 20-Oct-2015]. Environmental Protection Agency, “Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose.” Environmental Protection Agency, 09-Nov-2005. J. D. Yanosky, C. J. Paciorek, F. Laden, J. E. Hart, R. C. Puett, D. Liao, and H. H. Suh, “Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors,” Environ. Health, vol. 13, no. 1, p. 63, Aug. 2014. R. Gouripeddi, N. Sundar Rajan, R. Madsen, P. Warner, and J. C. Facelli, “Federating Air Quality Data with Clinical Data,” presented at the 2014 AMIA Annual Symposium Proceedings, 2014. “Pediatric Research Using Integrated Sensor Monitoring Systems | National Institute of Biomedical Imaging and Bioengineering.” [Online]. Available: http://www.nibib.nih.gov/research-funding/prisms. [Accessed: 20-Oct- 2015]. Acknowledgements Grants: UU Air Quality Program, U54EB021973, NCRR/NCATS UL1RR025764, 3UL1RR025764-02S2, AHRQ R01 HS019862, DHHS 1D1BRH20425, UU Research Foundation. CHPC at UU.
Improvement of Care in the Surgical Intensive Care Unit through Family Feedback
Avani Latchireddi, Wade Mather, and Joseph Tonna, University of Utah Life Sciences PurposeThis research project is to assess patient satisfaction and feedback around care provided in the Surgical Intensive Care Unit (SICU) at the University of Utah Hospital with the goal of iterative improvement of care. Research Question/HypothesisWe hypothesize that by assessing patient satisfaction of ICU care, we can implement directed changes targeting patient-identified concerns. MethodologyA survey of 37 questions, based on a validated national survey of family satisfaction with ICU care (FSICU-24) was put together addressing issues ranging from emotional care to technical aspects of the SICU experience on a whole. It is administered to the family member who was most involved in the patients care in the Surgical ICU after transfer out of the ICU. All the data is securely maintained and analyzed through a REDCap database for the purposes of quality improvement. ObservationsOver the initial weeks of administration, a few observations for improvement opportunities have been repetitive. Many patients and family members highly appreciate their attending doctors but cannot keep track of their names with the many teams of doctors. Having a time frame in which the doctor would arrive on rounds such that the family member can be present would be very helpful. The family members of patients sometimes feel uncared for in the SICU. Many would appreciate having someone show them the cafeteria or simply ask them if they need anything in particular. The plan of the day sheet (checklist as well as a list of the medical plan the team intends to follow) is often not given and/or explained to the patient and their family. ConclusionThe following changes will be considered for feasibility of implementation. Surveys will be continuously administered in order to observe the effect the implemented changes have had. For example, changes might include the nurses explaining the plan of the day sheet to the patient and their family after the doctor has stated the plan of care; having picture cards of doctors with their name and photo would help patients and families better identify their caregivers; a volunteer could go around the ICU once a day and ask if the family has any needs. The expectation is to see improved patient and family satisfaction in those selected areas.