A Review of Tools and Methods for Detection, Analysis, and Prediction of Allostatic Load Due to Workplace Stress
Bibliographic record
Abstract
Chronic stress risks an individual's overall well-being. Chronic stress is associated with allostatic load, the body's wear-and-tear due to prolonged heightened physiological and psychological states. Increased allostatic load among workers increases their risk of injuries and the likelihood of diseases and illnesses. An allostatic load model could explain the basis of a stress response. Stress research in affective computing uses wearable devices, data processing algorithms, and machine learning methods to create models that could benefit from an allostatic load model of stress. We emphasize the need for the allostatic load model in affective computing to create disease and illness prediction models. Predictive models could enhance safeguards in the workplace by helping to create proactive mitigation strategies against chronic stress. First, we briefly introduce allostasis’ physiological and psychological basis. Next, we reviewed stress studies within affective computing that may benefit from an allostatic load model of stress. We focused our review on studies conducted in dynamic settings, such as the workplace, and those incorporating typical stress study elements in affective computing. We conclude our review by identifying gaps between affective computing and neuroscientific stress studies and provide recommendations for adopting the allostatic load model of stress.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".