Investigating Higher Education Teachers’ Well-Being and Its Influencing Multiple Factors: A Systematic Review Approach
Bibliographic record
Abstract
Background: Teachers from higher education commonly face substantial workloads, resulting in heightened stress and reduced well-being. This has spurred significant academic interest in the determinants of faculty well-being within higher education. Consequently, numerous studies have focused on both understanding these influencing factors and developing strategies to bolster teacher well-being, an area that has gained considerable traction as a research focus. Although systematic reviews have been conducted to elucidate the connections between well-being and particular attributes like emotional regulation, efficacy, and competency, there remains a paucity of reviews that holistically examine the multifaceted factors affecting the well-being of university and college faculty. Methods: In this study, using a systematic review approach, we explore higher education teachers’ well-being (HETWB) and its influencing multiple-dynamic factors. Following PRISMA 2020, Web of Science and ERIC were selected as the meta-database, and 42 publications were obtained by strict criteria during literature screening. The basic information about the studies, including publication trends, regional distribution, method, and the factors affecting the well-being of college teachers, has been analyzed. Results: A review of the literature suggests a notable increase in scholarly attention directed towards teachers’ well-being in recent decades. Comparative bibliometric analysis identifies China, Spain, Canada, and the United States as the leading nations in terms of publications on this topic. Within this body of research, quantitative methods, particularly questionnaire-based surveys (n = 31), predominate as the primary investigative strategy. The determinants of teachers’ well-being are commonly categorized into several domains: demographic characteristics (e.g., gender, family status, university type/level, academic major, position, tenure); external work-related factors (e.g., availability of job resources, level of job demands); internal personal attributes (e.g., physical status, mental status, mental traits, mental competencies); interactive processes (e.g., work-home conflict/balance); and potential moderators (e.g., quality of work relationships). Additionally, there is a growing body of research dedicated to elucidating the multifaceted and dynamic mechanisms that govern the well-being of college faculty. Conclusion: Firstly, our global perspective allows us to move beyond the limitations of region-specific research and identify commonalities and variations in the factors influencing faculty well-being across diverse cultural and institutional contexts. Furthermore, we emphasize the bidirectional nature of these relationships, illustrating how well-being not only influences work outcomes (e.g., performance, and job satisfaction) but also receives impacts from them, creating a dynamic feedback loop. Finally, our inclusion of the counter-effect, where high levels of well-being, as conceptualized in HETWB, can lead to better psychological states, work performance, and access to resources, further enriches the understanding of this field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".