Measurement and Quantification of Stress in the Decision Process: A Model-Based Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it