Exploring COVID-19's Impact on Mental Health in the Workplace: A bibliometric analysis
Bibliographic record
Abstract
The objective of this paper is to examine important research areas and emerging development trends while highlighting the challenges and opportunities associated with them. This is accomplished through a methodical examination of publications pertaining to the mental well-being of employees during COVID-19. The authors analyzed 177 articles and contributions from the Scopus database that were listed by the Australian Business Dean Council (ABDC) using the bibliometric tool VOS viewer. According to the data, there have been increasing trends in the study of mental health and its effects during COVID-19, and psychology and human resource management are also seeing an increase in the study of mental health. The authors who have been referred the most are “Chawla N.,” “Mccarthy J.M.”, “Trougakos J.P.” and “Li J”. The “journal American Psychologist” has published a significant amount of research in psychology field and “International Journal of Hospitality Management” has maximum publications in Human resource management field. Using efficient bibliometric techniques, the authors give readers a thorough grasp of the research topic, which will be helpful to anybody interested in the field, particularly newcomers. They advise that future studies concentrate on creating hybrid models to forecast trends in particular fields of psychology and human resource management, which is a new field of study.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.087 | 0.088 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".