Profiles in workplace giving: a cluster analysis of ‘types’ of givers within a public university

This study examines public employees’ donations to a workplace giving campaign at a large public university in the south-east of the United States. First, we employed logistic regression to predict the likelihood of donating through workplace giving programmes using a sample of employees at a large public university (N = 11,726). Second, we estimated an ordinary least squares regression to identify the significant predictors of donation value with a subsample of employee donors (n=1,832). Third, we developed donor profiles (for example, clusters) of employee benefactors using K-medoids clustering. Factors such as sex, age, education and salary were significant predictors of both being a donor and the donation amount. Additionally, employment duration was significantly related to being a donor and the donation amount, while job classification only predicted being a donor. Employee donors fell into five distinct clusters. These findings contribute to our knowledge of workplace giving campaigns and can be used to develop strategic marketing campaigns.

The Improvement of Total Rewards and Working Condition for Taekwondo Instructor in the COVID-19 Era

The purpose of this study is to identify the overall difficulties of the Taekwondo industry due to the COVID-19 situation and to find ways to improve the total rewards and working condition so that Taekwondo instructors can play their roles while maintaining their self-esteem. The method of this study was a comparative study of the U.S. and Korea, which is the home country of Taekwondo. For this study, 12 American and Korean instructors were interviewed and additional surveys were conducted. As a result, both American and Korean Taekwondo instructors were playing their roles with high self-esteem. However, there were differences in job classification, allowances pay, incentives, and self-development opportunities. In U.S., Taekwondo instructors are clearly defined their tasks based on the job classification system and are applied well-designed overtime pay, incentive system and vacation plans. On the other hand, Korean instructors were working overtime and overworking without additional compensation to maintain long-term vision and employment relations. Comparative studies show that both countries have improvements to learn from each other. Taekwondo instructors in the U.S. need to increase their self-esteem in taekwondo while providing instructors with a vision for long-term careers. Taekwondo instructors in Korea need to improve total rewards and develop new skills such as mental training, counseling, and online lessons. The study will contribute to the overall improvement of total rewards, working condition and career development of taekwondo instructors in the U.S. and Korea.

The frequency of Raynaud’s phenomenon, very early diagnosis of systemic sclerosis, and systemic sclerosis in a large Veteran Health Administration database

Background We describe Raynauds phenomenon (RP), potential very early diagnosis of systemic sclerosis (VEDOSS), and systemic sclerosis (SSc) in Veterans deployed in support of Post-9/11 operations. We sought to describe the military occupation specialty, clinical features, and vasodilator use across the three diagnoses. Methods Individual Veterans medical records were assessed for RP (ICD-9443.0), VEDOSS with swelling of hands (ICD-9729.81) and RP (ICD-9443.0), and SSc (ICD-9710.1). The distribution of sociodemographic, military service branch, job classification, vasodilator use, and comorbidities were examined across the three classifications of disease. The chi-squared test and Fisher’s exact compared frequency of these categorical variables. Logistic regression assessed the likelihood of characteristics of the three classifications. Results In this population of 607,665 individual Veteran medical records, 857 had RP, 45 met possible VEDOSS criteria, and 71 had a diagnosis of SSc. The majority of RP, potential VEDOSS and SSc cases were white males. Those in craftworks, engineering or maintenance, and healthcare had a greater likelihood of RP. Less than half of RP and VEDOSS patients were on vasodilators. The most common comorbidities in this population were the diagnostic code for pain (highest in the potential VEDOSS group [81.6%]), followed by depression in all groups. Conclusion This is a unique Veteran population of predominately-male patients. Our data suggests that vasodilator medications are potentially being under-utilized for RP and potential VEDOSS. Our data highlights mood and pain management as an important aspect of SSc care.

Correlates of COVID-19 Vaccine Acceptance, Hesitancy and Refusal among Employees of a Safety Net California County Health System with an Early and Aggressive Vaccination Program: Results from a Cross-Sectional Survey

Since health professionals provide frontline care to COVID-19 patients, information on vaccine acceptance among healthcare workers is needed. We developed and implemented an anonymous internet-based cross-sectional survey with direct solicitation among employees of a safety net health system. Items queried demographic and health-related characteristics, experience with and knowledge of COVID-19, and determinants of decisions to vaccinate. COVID-19 vaccine acceptance groups (acceptors, hesitant, refusers) were defined; an adapted version of the WHO vaccine hesitancy scale was included. The survey demonstrated good reliability (Cronbach’s alpha = 0.92 for vaccine hesitancy scale; 0.93 for determinants). General linear and logistic regression methods examined factors which were univariately associated with vaccine hesitancy and vaccine acceptance, respectively. Multivariable models were constructed with stepwise model-building procedures. Race/ethnicity, marital status, job classification, immunocompromised status, flu vaccination and childhood vaccination opinions independently predicted hesitancy scale scores. Gender, education, job classification and BMI independently predicted acceptance, hesitancy, and refusal groups. Among hesitant employees, uncertainty was reflected in reports of motivating factors influencing their indecision. Despite a strong employee-support environment and job protection, respondents reported physical and mental health effects. The appreciation of varied reasons for refusing vaccination should lead to culturally sensitive interventions to increase vaccination rates amongst healthcare workers.

Correlates of COVID-19 vaccine acceptance, hesitancy and refusal among employees of a safety net California county health system with an early and aggressive vaccination program: Results from a cross-sectional survey

Information on vaccine acceptance among healthcare workers is needed as health professionals provide front line care to COVID-19 patients. We developed and implemented an anonymous internet-based cross-sectional survey with direct solicitation among employees of a safety net health system. Items queried demographic and health-related characteristics, experience with and knowledge of COVID-19, and determinants of decisions to vaccinate. COVID-19 vaccine acceptance groups (acceptors, hesitant, refusers) were defined; an adapted version of the WHO vaccine hesitancy scale was included. The survey demonstrated good reliability (Cronbachs alpha = 0.92 for vaccine hesitancy scale; 0.93 for determinants). General linear and logistic regression methods examined factors which were univariately associated with vaccine hesitancy and vaccine acceptance, respectively. Multivariable models were constructed with stepwise model-building procedures. Race/ethnicity, marital status, job classification, immunocompromised status, flu vaccination and childhood vaccination opinions independently predicted hesitancy scale scores. Gender, education, job classification and BMI independently predicted acceptance, hesitancy and refusal groups. Among hesitant employees, uncertainty was reflected in reports of motivating factors influencing their indecision. Despite a strong employee-support environment and job protection, respondents reported physical and mental health effects. Appreciation of varied reasons for refusing vaccination should lead to culturally sensitive interventions to increase vaccination rates in healthcare workers.

Development of a Job Applicants E-government System Based on Web Mining Classification Methods

Governmental establishments are maintaining historical data for job applicants for future analysis of predication, improvement of benefits, profits, and development of organizations and institutions. In e-government, a decision can be made about job seekers after mining in their information that will lead to a beneficial insight. This paper proposes the development and implementation of an applicant's appropriate job prediction system to suit his or her skills using web content classification algorithms (Logit Boost, j48, PART, Hoeffding Tree, Naive Bayes). Furthermore, the results of the classification algorithms are compared based on data sets called "job classification data" sets. Experimental results indicated that the algorithm j48 had the highest precision (94.80%) compared to other algorithms for the aforementioned dataset.

Prediction of Problematic Smartphone Use: A Machine Learning Approach

While smartphone addiction is becoming a recent concern with the exponential increase in the number of smartphone users, it is difficult to predict problematic smartphone users based on the usage characteristics of individual smartphone users. This study aimed to explore the possibility of predicting smartphone addiction level with mobile phone log data. By Korea Internet and Security Agency (KISA), 29,712 respondents completed the Smartphone Addiction Scale developed in 2017. Integrating basic personal characteristics and smartphone usage information, the data were analyzed using machine learning techniques (decision tree, random forest, and Xgboost) in addition to hypothesis tests. In total, 27 variables were employed to predict smartphone addiction and the accuracy rate was the highest for the random forest (82.59%) model and the lowest for the decision tree model (74.56%). The results showed that users’ general information, such as age group, job classification, and sex did not contribute much to predicting their smartphone addiction level. The study can provide directions for future work on the detection of smartphone addiction with log-data, which suggests that more detailed smartphone’s log-data will enable more accurate results.

Between Two Crises

The purpose of this chapter was to critically examine the reconstruction of professional identity between two crises: The Great Recession of 2008 and the COVID-19 pandemic of 2020. Using a critical participatory action research self-study design, the author deconstructs the transition from for-profit behavioral health care business leadership to adjunct professor. Data sources include U.S. government job classification profiles, syllabi from courses taught, and the university's corresponding student surveys to answer the primary research question: How can teaching action research contribute to the reshaping of professional identity? Data analysis revealed that iterative cycles of reflection and action in teaching action research supported the development of identity as an academic across time. The knowledge, skills, and abilities required for a career in business supported, and also interfered with, career transition. Lastly, the author understood that the problem of practice stemmed from lack of recognition of her own privilege.