Seeking Approval from Universities to Research the Views of Their Staff. Do Gatekeepers Provide a Barrier to Ethical Research?

A “gatekeeper” controls access to an organization; “gatekeeper approval” is often needed before external research can take place within an organization. We explore the need for gatekeeper approval for research with university staff employing, as a case study, a project which collected data in Australia. This case study addresses known issues, seemingly rarely addressed in the literature. The Human Research Ethics Committee (HREC)'s requirement for approval from individual universities to approach their staff brought significant consequences, exacerbated by the lack of university procedures for such approvals. Simultaneously, since invitations could legitimately be distributed via other avenues, such approval was superfluous. We recommend the HREC's blanket requirement for institutional approval instead be considered on a case-by-case basis depending on the risk of the research, and perhaps waived for low-risk research where participants are able to provide informed consent, and that universities establish processes to deal with requests from external researchers.

Academic Writing for Publication Purposes and Machine Translation: Is the Symbiosis Possible?

The abstracts in Russian and in English are written according to certain rules. For articles written in Russian, an abstract in English is the only means of informing the world scientific community about the authors’ research results. However, the quality of abstracts written in Russian and then translated into English does not always meet the criteria for readability and comply with the accepted academic and publication conventions. This situation might result from the intensive usage of machine translation (MT) systems by authors who do not take into account the guidelines for the input text quality and the limitations inherent in MT systems. The author analyzed the requirements for the input texts and some typical errors in the target ones. The article describes the stages of training masters, postgraduate students and university staff in the effective use of MT systems. The training is based on a bilingual approach, which implies a constant comparison of vocabulary, grammar and style in the native and English languages. The author comes to the conclusion that the effective use of MT systems for writing an abstract in English is possible if the authors have the command of Russian and English at the level sufficient for a concise and unambiguous expression of their thoughts and ideas. Self-censoring is a prerequisite for creating a text that is “understandable” for MT systems. Students must follow simple rules: write sentences of 15-20 words; express one thought per one sentence; use more active verbs; choose nouns that express a specific concept; exclude unnecessary words. At the post-editing stage, learners can use the tools available on the Internet, which allow not only editing the target text, but also will enable learners to acquire independent editing skills.

Association between Health-Related Physical Fitness and Risk of Dyslipidemia in University Staff: A Cross-Sectional Study and a ROC Curve Analysis

Background: This study aimed to assess the relationship between dyslipidemia (DL) risk and health-related physical fitness (HPF) and evaluated the prognostic value of HPF for risk of DL. Methods: A total of 776 university staff members were recruited, of which 407 were females, and 369 males. Blood samples and HPF tests were collected from all participants after 12 h fasting. Results: The prevalence of DL was 41.77% and 51.49% in female and male university staff members, respectively, and there was no significant difference between genders (χ2 = 2.687, p = 0.101). According to the logistic regression analysis, age, male sex, GLU, hypertension, BMI, BF, WHtR, and LAP were significant risk factors for DL (p < 0.05), VCI and, SAR were significant protective factors for DL (p < 0.05), and SMI, GS, and VG were not significantly associated with the risk of DL. The area under the receiver-operating characteristic (ROC) curve (AUC) analysis indicated that, LAP (AUC: 0.730, 95CI%: 0.697–0.762), WHtR (AUC: 0.626, 95CI%: 0.590–0.660), and BMI (AUC: 0.599, 95CI%: 0.563–0.634) are valid predictors of DL, and LAP and WHtR perform better than BMI (Z = 8.074, p < 0.001) in predicting DL in male and female university staff members. Conclusion: The risk of DL is significantly related to body composition, cardiorespiratory fitness, and flexibility. LAP and WHtR perform better than BMI in predicting risk of DL in male and female university staff members.

A brief, daily, online mental health and wellbeing intervention for university staff during the COVID-19 pandemic: Program description and outcomes using a mixed methods design (Preprint)

BACKGROUND The unprecedented changes and isolation measures to contain the coronavirus disease (COVID-19) have had multiple psychological and social impacts, with implications for professional and personal functioning. Evidence-informed interventions that can be rapidly implemented under pandemic conditions to support mental health during such times were urgently needed. OBJECTIVE This study aimed to determine the acceptability and preliminary outcomes of a daily online mental health promotion program for tertiary education staff during the COVID-19 pandemic. METHODS The ‘VU Elevenses’ program was delivered as an uncontrolled intervention at Victoria University (VU) in the western metropolitan region of Melbourne, Australia. In April 2020, an email invitation was sent to all academic and professional staff inviting them to: 1) participate in the program; and 2) opt-in to the research component. The ‘VU Elevenses’ program provided 10-15-minute micro-interventions comprising lifestyle and wellbeing strategies to promote mental health, via an online meeting platform, at 11am each weekday. A mixed-method approach was used to evaluate the program, combining structured questionnaires with semi-structured interviews to investigate the experiences of staff who participated in the program. RESULTS Between 16-90 participants provided weekly program feedback. A total of 106 university staff opted into the longitudinal research component and 10 staff participated in the interviews. Participants reported high levels of satisfaction with sessions and perceived benefits for mental health. Approximately one quarter of participants reported moderate to severe symptoms of depression, anxiety, and stress at baseline, with significant reductions in these symptoms in the first seven weeks of the program, corresponding with easing in mandatory isolation (‘lockdown’) restrictions. However, symptoms of depression, anxiety and stress all increased when lockdown measures were reintroduced, but not to the same levels as the initial lockdown period. Overall changes in depression and anxiety from baseline to the end of the program were explained by changes in COVID related distress, whereas changes in self-compassion explained changes in stress. CONCLUSIONS We show that it is feasible and acceptable to develop and deliver a program of brief interventions in a timely manner, using a simple and accessible online platform. Although participation in the program was initially associated with reduced symptoms of depression, anxiety and stress, participants’ mental health worsened with the reintroduction of a ‘lockdown’ period. However, as symptoms of depression, anxiety and stress did not return to levels observed at the start of the VU Elevenses program, participation in the uncontrolled intervention may have offered a protective benefit against the impact of the second significant lockdown period. CLINICALTRIAL N/A

Flying High in Academia—Willingness of University Staff to Perform Low-Carbon Behavior Change in Business Travel

This study aims to explore the factors that influence business travel decisions of university staff, in particular the extent and ways in which they are willing to reduce emission-intensive air travel, and the personal and structural barriers to such behavior change. Three strategies to reduce air travel were investigated: abstaining from particular events, substituting travel through virtual participation and mode shifting to ground-based public transport. We tested the effects of (1) specific decision factors for engaging in long-distance travel, choosing specific modes of travel and choosing virtual solutions; (2) former travel activities; (3) postponed trips due to COVID-19; and (4) sociodemographic factors, on the willingness of individuals to reduce air travel in a sample of university employees. We calculated regression models for the three strategies and added a qualitative analysis of open-ended comments. Former travel behavior as well as pro-environmental considerations play significant roles, influencing the willingness of employees to change their business travel behavior. Furthermore, we found that willingness to reduce air travel depends on the scope of behavior change. Although travel behavior is unevenly distributed across different subgroups, sociodemographic factors only play a minor role in the regression models. The present study adds to the limited body of quantitative research on the reduction potential of academic air travel, presenting an examination of university staff's willingness to change their long-distance travel behavior. Implications for university polices are discussed.

Health Utility and Its Risk Factors in University Staff in China: A Cross-Sectional Survey from an Occupational Health Perspective

Backgrounds: The health of university staff is a major occupational health concern worldwide. Studies have reported low health-related quality of life (HRQOL), low job satisfaction and poor mental health in this occupational group. However, none of previous studies have measured health utility and compared it to a national norm. Therefore, this study was conducted to gain a deeper understanding of the HRQOL of university staff in China and to identify risk factors influential to their health. Methods: This was a cross-sectional survey conducted in a public university in China. Participants were interviewed face-to-face for demographic and socioeconomic information and health conditions. The Chinese version of the EQ-5D-5L instrument was used to measure HRQOL for calculating health utility. The relationship between health utility and sample characteristics was first examined using t-test and correlation analysis. Multivariate generalized linear models were further applied to evaluate the significance of these associations while adjusting for other variables. Results: The sample (n=154) had a mean age of 40.65 years and slightly more females (51.30%). The overall prevalence of diseases or symptoms was 81.17%. Participants attained the means (SDs) of 0.945 (0.073) and 83.00 (11.32) for the health utility and visual analogue scale respectively. The most affected domain was the anxiety/depression with 40.26% of participants reporting problems and 37.66% of the sample reported problems in the pain/discomfort domain. There were less than 5% participants reported problems in the mobility, self-care or daily activity domains individually. Multivariate models revealed that psychological/emotional conditions were associated with the largest utility loss of -0.067 (95%CI: -0.089, -0.045) followed by having a Master’s degree or higher (-0.048, 95%CI: -0.09, -0.005) and pain in body parts other than head, neck and back (-0.034, 95%CI: -0.055, -0.014).Conclusions: University staff in China may have worse HRQOL than the general population, which manifested mainly with the pain/discomfort and anxiety/depression domains. The significant factors for utility loss were having a Master’s degree or higher, psychological conditions and pain in body parts other than the head, neck and back. Targeted health promotion policies and programs should be created to benefit this occupational group and society overall.

Research of the applicability of machine learning methods for assessment of departments’ performance

Currently, a number of educational organizations in Russia and abroad, including the National Research University “Moscow Power Engineering Institute” (MPEI), are introducing the European improvement model EFQM, designed to analyze and improve the educational, scientific and other activities of the departments. In accordance with this model, each university department is assigned a score equal to the sum of points for two groups of criteria: criteria from the group of opportunities and criteria from the group of results. To obtain such assessments, a commission consisting of external experts, EFQM assessors and university staff meets with heads of departments. Based on the results of the discussion of the results of the meetings, the commission determines the score and rating of the departments in accordance with the EFQM model.The purpose of the work presented in the article is to study the possibility of using machine learning to simplify the work of experts in terms of obtaining estimates according to criteria from a group of results.The article proposes a system for evaluating the activities of departments according to criteria from a group of results based on machine learning. A program in the Python programming language has been developed, which evaluates the activities of departments according to these criteria for each department of the MPEI. The program receives the initial data for such assessments from the monitoring system of key performance indicators implemented in MPEI.