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Dishonesty and misconduct of nursing students during their education is a continuing concern, especially as it relates to expected professional behaviors. This quasi experimental pre-and post-interventional study explored the impact of an affective learning module on developing beliefs and perceptions of ethicality of academic and professional misconduct of students in a BSN program. A modified version of McCrink’s (2008) tool, entitled “Nursing Student Experiences and Perceptions of Academic and Professional Conduct Survey” measured these constructs. The affective learning module, based on Rest’s theory of moral development, and Krathwohl’s affective taxonomy, actively engaged students in ethical decision-making with connections to professional practice through discussion, case review and reflection. The pretest data (N=65) analysis using Mann Whitney U, indicated differences in mean rank related to enrollment status, healthcare work experience and gender and beliefs, and perceptions of academic and professional misconduct. A comparative analysis (N=16) of pre and posttest data was limited due to required changes related to the COVID-19 pandemic. Changes in pre and posttest data suggest the need to replicate and continue research in the area of intentional affective teaching of ethicality and professional values.
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A Thesis Submitted to the School of Graduate and Professional Studies in Partial Fulfillment of the Requirements for the Degree of Master of Science in Biology Southern Connecticut State University New Haven, Connecticut December 2021 Abstract: In order to properly assess current ecological conditions, we need long-term ecological data. Historical ecology focuses on that long term, including the need to synthesize data from diverse sources. In the Long Island Sound, the Connecticut Department of Energy and Environmental Protection has been collecting data for both scientific and recreational purposes for decades, but the format of the recreational data (narrative) is not suitable for scientific analysis. This project is to collate and annotate game fish occurrence data from the Fishing Report newsletters put out by DEEP every week during the fishing season and the DEEP Trophy Fish annual reports, over a 12-year period. Species, location, and measurement data (as available) have been compiled into a data set, with geolocation coordinates added for the identifiable locations. This thesis consists of the machine-readable dataset, the protocol for collating this data, and an assessment of the suitability of the data for different kinds of analysis. The dataset will be published openly for reuse, reanalysis, and collaborative additions.
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