Bibliographic record
Abstract
Work-related stress is costly not only to employees, but also to organizations and society. For example, it is estimated that work-related stress, depression, and anxiety costs British employers £1,035 per employee and that workplace stress costs the US economy up to $300 billion annually. However, elevated levels of stress often cannot be changed, and, if demands were not placed on employees, employee learning, organizational innovation, and societal economic growth would be hindered. Consequently, it is vital that occupational health practitioners, employees, employers and researchers strive to better understand and manage workplace stress, such that employee health and well-being can be improved.This book can assist organizations and individuals as they encounter workplace stress. This edition highlights research done by 25 authors across 12 chapters that challenges how work stress is viewed and assessed. Additionally, a number of social and psychological influences on the stress experience are examined. Our beliefs and expectations of stress and its results, whether helpful or hurtful, can have a profound influence on our stress experiences. Also, the way that we approach our work (e.g., job crafting) or the treatment we receive from others (e.g., with dignity) can either mitigate or exacerbate any harmful or beneficial effects of stress. Moreover, how we assess the psychological (e.g., burnout and well-being) or physiological (e.g., cortisol) outcomes of stress are meaningful, and the proper diagnosis of stress (e.g., stress surveys) underlies our understanding. We hope that the findings reported in these chapters and the insights of these scholars will provide ways for you and/or your organization to improve the health and well-being of employees.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".