How to manage stress at the workplace: neuroscientific applications
Bibliographic record
Abstract
FRAMEWORK OF STRESS AT THE WORKPLACEHistorically, stress has been defined as an aspecific response of the organism to any form of exogenous or endogenous stimulus that, due to its duration or intensity, is capable of triggering adaptation mechanisms to face the stimulus and restore homeostasis (Selye, 1975).Despite this definition could be criticized as too simplistic, since it leaves out the contextual factors and appraisal mechanisms (Matthews et al., 2017), it contains the assumption that stress can be considered a basic adaptation reaction to a positive or negative event.Stress responses are essential for triggering the organism and for addressing an event or situation appropriately.In this way, stress encourages a controlled reaction in which the individual feels to have sufficient skills and resources to respond to the context's demands and initiates a productive problem-solving process.On the other hand, when the action of the stressor agent is too severe and lasts for long, such processes of physiological adaptation begin to fail, homeostatic regulatory mechanisms become less efficient, and the stress response becomes dysfunctional (Dhabhar, 2014).Therefore, extremely low, and extremely high stress levels are linked to a drop in cognitive performance and the lack of adaptive responses, while optimal levels of performance are settled in the middle of the stress curve.Intense and chronic exposure to severe stressors may have relevant clinical implications, however, the same sequence of physiological and psychological events occurs
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".