Measurement and Quantification of Stress in the Decision Process: A Model-Based Systematic Review
Bibliographic record
Abstract
This systematic literature review comprehensively assesses the measurement and quantification of decisional stress using a model-based, theory-driven approach. It adopts a dual-mechanism model capturing both System 1 and System 2 thinking. Mental stress, influenced by factors such as workload, affect, skills, and knowledge, correlates with mental effort. This review aims to address 3 research questions: (a) What constitutes an effective experiment protocol for measuring physiological responses related to decisional stresses? (b) How can physiological signals triggered by decisional stress be measured? (c) How can decisional stresses be quantified using physiological signals and features? We developed a search syntax and inclusion/exclusion criteria based on the model. The literature search we conducted in 3 databases (Web of Science, Scopus, and PubMed) resulted in 83 papers published between 1990 and September 2023. The literature synthesis focuses on experiment design, stress measurement, and stress quantification, addressing the research questions. The review emphasizes historical context, recent advancements, identified knowledge gaps, and potential future trends. Insights into stress markers, quantification techniques, proposed analyses, and machine-learning approaches are provided. Methodological aspects, including participant selection, stressor configuration, and criteria for choosing measurement devices, are critically examined. This comprehensive review describes practical implications for decision-making practitioners and offers insights into decisional stress for future research.
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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 | 0.030 | 0.126 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 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".