Remaking higher education for the post‐COVID‐19 era: Critical reflections on marketization, internationalization and graduate employment

Professional Identity among New Rehabilitation Graduates in China: A Cross-Sectional Questionnaire-Based Survey

Background

The rehabilitation industry suffered in a terrible economic climate caused by the coronavirus disease 2019 (COVID-19). Healthcare resources and the labor force were directed towards epidemic prevention of post-pandemic, which exacerbated the issue. This study evaluated the professional identity (PI) of new graduates majoring in rehabilitation therapy during the final harsh phase of COVID-19 and explored the factors influencing PI.

Methods

A cross-sectional investigation with a questionnaire was used in this study. A convenience sample of new graduates who majored in rehabilitation were recruited from hospitals and universities nationwide. The data collected demographic information and scores of professional identities for students in the final phase of the strict COVID-19 control policy in 2022.

Results

This study indicated that the average PI was quite a distance from the degree of 'agree' (3.84 on average), particularly for the fitness dimension (3.64 on average). The results identified two influencing variables: employment (Beta= -0.09, P< 0.05) and educational patterns (Beta= 0.12, P< 0.01).

Conclusions

Higher education in rehabilitation should respond to the present shift in the structure of the rehabilitation industry post-COVID-19. Meanwhile, the occupational environment of rehabilitation therapists and the medical value of rehabilitation therapy deserves more attention. This study provides evidence for managers to improve organizational justice and adjust policies on the distribution of medical resources.

A bilateral employment situation prediction model for college students using GCN and LSTM

Due to the prevailing trend of globalization, the competition for social employment has escalated significantly. Moreover, the job market has become exceedingly competitive for students, warranting immediate attention. In light of this, a novel prognostic model employing big data technology is proposed to facilitate a bilateral employment scenario for graduates, aiding college students in promptly gauging the prevailing social employment landscape and providing precise employment guidance. Initially, the focus lies in meticulously analyzing pivotal aspects of college students' employment by constructing a specialized employment platform. Subsequently, a classification model grounded in a graph convolution network (GCN) is built, leveraging big data technology to comprehensively comprehend graduates' strengths and weaknesses in the employment milieu. Furthermore, based on the outcomes derived from the comprehensive classification of college students' qualities, a college students' employment trend prediction method employing long and short term memory (LSTM) is proposed. This method supplements the analysis of graduates' employability and enables accurate forecasting of college students' employment trends. Empirical evidence substantiates that my proposed methodology effectively evaluates graduates' comprehensive qualities and successfully predicts their employment prospects. The achieved F-values, 82.45% and 69.89%, respectively, demonstrate the efficacy of anticipating the new paradigm in graduates' dual-line employment.

A conditional time-varying multivariate latent growth curve model for the relationships between academics' servant leadership behavior, affective commitment, and job performance during the Covid-19 pandemic

In our study, we investigated the change patterns in academics' servant leadership behavior and affective commitment during and after the first Covid-19 lockdown (April 2020-August 2020) in Malaysia. Furthermore, we evaluated the influence of academics' servant leadership behavior on job performance through affective commitment. We applied additional analyses to determine the degree to which the two former constructs are influenced by age and academic rank. To do so, we leveraged multivariate latent growth curve (LGC) modeling in analyzing the longitudinal data collected from 220 academics at three time points over a course of four months during the Covid-19 pandemic. Our results showed that, while servant leadership and affective commitment were stable over time, servant leadership was a driver of affective commitment and job performance. We also observed that affective commitment significantly mediated the relationship between academics' servant leadership behavior and job performance. Moreover, we found interindividual differences in servant leadership and affective commitment in terms of age and academic rank at the beginning of the Covid-19 lockdown. Our multi-disciplinary research is among the limited number of studies applying a rigorous, longitudinal approach to examine behaviors (i.e., servant leadership and job performance) and attitudes (i.e., affective commitment) during the Covid-19 pandemic in higher education research.

Socioeconomic Status of University Students as a Limiting Factor for Various Forms of Distance Education during COVID-19 Measures

The aim of this study was to determine the influences and impacts of the socioeconomic status (SES) of families on university students and their studies during anti-pandemic measures resulting from the COVID-19 pandemic. Another aspect of the study was to investigate the various educational elements used during the course of distance teaching. This research was performed on a sample of 1280 respondents. Statistically significant differences in the case of SES grades were observed only with the following variables: (i) difficulty factor for the combined education form, (ii) age, and (iii) the duration of the teaching practice (p<0.05). The post-hoc analysis (LSD test) did not show a linear trend in the sense that the values of the respective variables also increased as the SES increased. These data proved that the higher the economic status of the family, the lower the value of the variable.

Job satisfaction, academic motivation, and organizational citizenship behavior among lecturers during the COVID-19 pandemic: a cross-national comparative study in Japan and Malaysia

Job satisfaction and its antecedents and outcomes are important areas of focus in the social sciences research, and higher education is no exception. The importance of this issue has grown during the COVID-19 pandemic. For this reason, using a cross-national study conducted in Malaysia and Japan, we collected data on lecturers’ job satisfaction and two of its outcomes, namely, academic motivation and individual-level organizational citizenship behavior (OCBI) to test our evidence-based theoretical model, which explains the relationships between these variables. We also added age, gender, and tenure as covariates to our model. Our partial least squares structural equation modeling estimation results at the aggregate and country levels showed that the effect of job satisfaction on OCBI was mainly transmitted through academic motivation. We also observed that Malaysian and Japanese lecturers did not show a statistical difference in terms of the relationships described between the variables in our model. Additionally, the relationship between academic motivation and OCBI was nonlinear based on the data from the Malaysian sample, and we explained this phenomenon from both theoretical and practical/policy perspectives. Moreover, our results showed that age plays an important role in the model when it is estimated using data from the Malaysian higher education system. We discussed our findings in detail in terms of theoretical and practical implications.