Workplace interventions for occupational stress
Bibliographic record
Abstract
Research consistently has documented the same phenomenon across the developed world: as the pace of competition increases and we enter a truly global marketplace, stress and its consequences are becoming epidemic (Sauter, Murphy, and Hurrell, 1990). Increased work hours, increased pressure, increased insecurity (e.g. Bond, Galinsky, and Swanberg, 1997), and myriad other organizational stressors have immediate and long-term consequences for both individuals and organizations. Consequently, it is not surprising that research on work stress has proliferated. However, the question of what organizations can do to avert or mitigate the negative consequences of stress – arguably the single most important question in the field – remains largely unaddressed and, therefore, unanswered. In this chapter, we summarize what is known about how organizations can deal with the proliferation of organizational stressors. In doing so, we also attempt to identify what is not known – thereby establishing agendas for both practice and research. A model of job stress A variety of “models” of job stress exist, varying in both their breadth and the complexity of the processes underlying the model predictions (Kelloway and Day, 2005a). Despite diverse theoretical approaches, we suggest that most work stress researchers would agree with a basic model postulating that a set of organizational stressors (i.e. events that occur in the work environment outside the individual; Pratt and Barling, 1988) may be perceived as stressful by the individual, and consequently can result in a variety of strain reactions (see, for example, Hurrell and Kelloway, in press).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".