Resilience and Stress at Professional Work: Analysis of the Research Landscape and Public Interest
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Stress has a negative impact on the efficiency and productivity of employees and, accordingly, brings additional costs or losses to companies. A company’s proactive role in ensuring its employees’ resilience to stress in the workplace is crucial in creating an effective working environment and reducing staff turnover. The article analyses the scientific environment (using Descriptive bibliometric analysis and Science Mapping) and public interest (using Google Trends) in resilience and stress at professional work and in management in this area. The bibliometric analysis was conducted using VOSviewer based on publications from the Scopus database (Elsevier) without restrictions on the year of publication, country, author, language and category; the trend analysis was conducted for the period 2004‒2024 for all categories and countries, all Internet traffic was restricted to “Web searches”. The study showed a steady increase in Internet searches for the concept of “resilience training”, with peaks in 2004 and 2005 (coinciding with known terrorist attacks); interest in the query “resilience at professional work” is highest in the United States and the United Kingdom, and 12 leading countries were identified for the query “stress at professional work”. The dynamics of scientific papers in this area are described in the article by a 3rd-degree polynomial dependence equation with a determination coefficient of R² = 0.9676; a significant surge in publication activity occurred in 2014, with the number of papers increasing by 472% and an annual growth rate of 20% in 2014‒2024. The dominant subject areas of these publications are medicine (34.50%), social sciences (15.08%) and psychology (14.47%); countries are the USA, the UK, Australia, Canada, and China. The article identifies scientists who are leaders of scientific thought in this area, the most developed research networks, authoritative publications and journals (based on citation analysis), the most powerful international and national institutions that have funded research in this area (among the world leaders are the US National Institutes of Health and the US Department of Defence). The article structures the scientific work in this area: 1) based on the content and contextual feature, nine thematic clusters are identified, the most significant of which is the one that studies stress at work and professional burnout from a medical point of view; 2) based on the content and chronological feature, the most significant number of works were those on the human factor and gender issues of stress and resilience published in 2017‒2018.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it