Browsing by Author "McCrory, Bernadette"
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Item Enhancing Language Comprehension in Neurodivergent Children of Rural Communities: A Multimodal Approach(Undergraduate Scholars Program, 2024-04) Hughes, Mackenzie; Modyanova, Nadya; Pliska, Kate; Wave, Kiaya; Storruster, Carter; McCrory, BernadetteThis research investigates the efficacy of incorporating gestures in enhancing language comprehension, particularly of determiners (e.g., “the”), in neurodivergent children from rural communities. Utilizing Electroencephalograms (EEGs) alongside eye-tracking technology, the study aims to provide deeper insights into the cognitive processes involved in language comprehension among children with Autism Spectrum Disorder (ASD) and Developmental Language Disorder (DLD). Existing studies highlight EEG abnormalities in neurodivergent children, underscoring the necessity of tailored interventions. However, past research often overlooks non-verbal and partially verbal individuals, particularly in rural areas, exacerbating accessibility barriers. Addressing this gap, our study employs an inclusive approach, involving non-verbal rural participants, to offer a more comprehensive understanding of language comprehension in neurodivergent children. Through the integration of gestures and determiners, the study hypothesizes improved comprehension, especially among children with ASD and younger typically developing (TD) children. Eye tracking complements EEG data, enabling the observation of gaze patterns and correlations with cognitive and language skills. Notably, EEG data analysis aims to identify semantic processing differences and abnormal brain wave patterns, potentially serving as biomarkers for ASD and DLD. This research builds upon previous findings, emphasizing the positive impact of multimodal interventions on language comprehension. Collaborative efforts with experienced researchers and utilization of innovative technologies enhance the study's robustness and potential for real-world application. Ultimately, this study aims to inform the development of effective interventions tailored to the unique needs of neurodivergent children in rural communities, thereby improving their quality of life and fostering inclusive practices in language development research and intervention. Data collection and analysis is ongoing, and future results will be reviewed.Item Predicting Length of Stay using machine learning for total joint replacements performed at a rural community hospital(Plos One, 2022-11) Sridhar, Srinivasan; Whitaker, Bradley; Mouat-Hunter, Amy; McCrory, BernadetteBackground. Predicting patient’s Length of Stay (LOS) before total joint replacement (TJR) surgery is vital for hospitals to optimally manage costs and resources. Many hospitals including in rural areas use publicly available models such as National Surgical Quality Improvement Program (NSQIP) calculator which, unfortunately, performs suboptimally when predicting LOS for TJR procedures. Objective. The objective of this research was to develop a Machine Learning (ML) model to predict LOS for TJR procedures performed at a Perioperative Surgical Home implemented rural community hospital for better accuracy and interpretation than the NSQIP calculator. Methods. A total of 158 TJR patients were collected and analyzed from a rural community hospital located in Montana. A random forest (RF) model was used to predict patient’s LOS. For interpretation, permuted feature importance and partial dependence plot methods were used to identify the important variables and their relationship with the LOS. Results. The root mean square error for the RF model (0.7) was lower than the NSQIP calculator (1.21). The five most important variables for predicting LOS were BMI, Duke Activity Status-Index, diabetes, patient’s household income, and patient’s age. Conclusion. This pilot study is the first of its kind to develop an ML model to predict LOS for TJR procedures that were performed at a small scale rural community hospital. This pilot study contributes an approach for rural hospitals, making them more independent by developing their own predictions instead of relying on public models.Item Rural implementation of the perioperative surgical home: A case-control study(Baishideng Publishing Group Inc., 2023-03) Sridhar, Srinivasan; Mouat-Hunter, Amy; McCrory, BernadetteBACKGROUND. Perioperative surgical home (PSH) is a novel patient-centric surgical system developed by American Society of Anesthesiologist to improve outcomes and patient satisfaction. PSH has proven success in large urban health centers by reducing surgery cancellation, operating room time, length of stay (LOS), and readmission rates. Yet, only limited studies have assessed the impact of PSH on surgical outcomes in rural areas. AIM. To evaluate the newly implemented PSH system at a community hospital by comparing the surgical outcomes using a longitudinal case-control study. METHODS. The research study was conducted at an 83-bed, licensed level-III trauma rural community hospital. A total of 3096 TJR procedures were collected retrospectively between January 2016 and December 2021 and were categorized as PSH and non-PSH cohorts (n = 2305). To evaluate the importance of PSH in the rural surgical system, a case-control study was performed to compare TJR surgical outcomes (LOS, discharge disposition, and 90-d readmission) of the PSH cohort against two control cohorts [Control-1 PSH (C1-PSH) (n = 1413) and Control-2 PSH (C2-PSH) (n = 892)]. Statistical tests including Chi-square test or Fischer’s exact test were performed for categorical variables and Mann-Whitney test or Student’s t-test were performed for continuous variables. The general linear models (Poisson regression and binomial logistic regression) were performed to fit adjusted models. RESULTS. The LOS was significantly shorter in PSH cohort compared to two control cohorts (median PSH = 34 h, C1-PSH = 53 h, C2-PSH = 35 h) (P value < 0.05). Similarly, the PSH cohort had lower percentages of discharges to other facilities (PSH = 3.5%, C1-PSH = 15.5%, C2-PSH = 6.7%) (P value < 0.05). There was no statistical difference observed in 90-d readmission between control and PSH cohorts. However, the PSH implementation reduced the 90-d readmission percentage (PSH = 4.7%, C1-PSH = 6.1%, C2-PSH = 3.6%) lower than the national average 30-d readmission percentage which is 5.5%. The PSH system was effectively established at the rural community hospital with the help of team-based coordinated multi-disciplinary clinicians or physician co-management. The elements of PSH including preoperative assessment, patient education and optimization, and longitudinal digital engagement were vital for improving the TJR surgical outcomes at the community hospital. CONCLUSION. Implementation of the PSH system in a rural community hospital reduced LOS, increased direct-to-home discharge, and reduced 90-d readmission percentages.Item Trait emotional intelligence in American pilots(Springer Nature, 2022-09) Dugger, Zachary; Petrides, K. V.; Carnegie, Nicole; McCrory, BernadetteThere is a dearth of trait emotional intelligence (trait EI) research within an aviation context. Using the Trait Emotional Intelligence Questionnaire (TEIQue), the present study investigated potential trait EI differences between pilots and general population controls in the United States. The forty-four pilots who volunteered to participate were primarily male (93%) and between 24 and 67 years with a wide range of flight experience (150–5000 + hrs.) They were matched with controls based on age, gender, and ethnicity. Comparisons on global trait EI and the four trait EI factors revealed significant differences, with pilots scoring consistently lower than their matched counterparts in global trait EI, Well-being, Emotionality, and Sociability, but not Self-control. Overall, the findings indicated that pilots felt less connected to their emotional world than controls. Though limited by sample size and participant diversity, the results provide a basis for future studies into the trait EI profile of pilots, which had not been previously investigated.