Predicting Stress among Pedestrian Traffic Workers Using Physiological and Situational Measures
Bibliographic record
Abstract
Traffic workers are vulnerable to accidents and must make critical decisions to avoid conflicts between road users. This can lead to high stress levels, which may hinder their capacity to mitigate the occurrence of hazards. Measuring stress on the field could represent an efficient solution to help pinpoint risky situations and identify factors that increase risk. The goal of this study was to verify whether stress among traffic workers could be predicted using physiological measures and characteristics of the work situation. Nineteen police officers in Quebec City and Montreal, Canada, performed traffic duties while their physiological activity was assessed by a wearable physiological harness. Every 15 minutes, change in subjective stress was also measured. Results showed that decision-tree models outperformed multifactorial logistic regressions for predicting subjective stress based on both situational factors and physiological measures. This demonstrated the potential of using such measures to monitor stress among traffic workers.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".