Understanding and preventing bypassing of safeguards on machinery through an assessment tool based on probability levels
Bibliographic record
Abstract
The Act Respecting Occupational Health and Safety is a law in Quebec aiming at eliminating, at their source, dangers to the health, safety and physical well-being of workers.Therefore, ensuring occupational health and safety (OHS) will contribute to protecting workers from risks and entering a hazard zone of machinery; subsequently, it will maintain and promote the highest degree of physical, mental, and social well-being of workers in all professions.Millions of employees working on machines may be exposed to various hazards, including mechanical, electrical, thermal, noise, vibration, radiation, material or contamination, ergonomic, and environmental hazards, during their interventions on machinery.Those hazards can cause serious injuries or fatalities if they are not well-managed.Therefore, in the process of occupational risk management, measures are essential to control the OHS-related risks.Guards and protective devices are the most efficient risk reduction measures, after inherently safe design, in the hierarchy of risk reduction measures associated with machines.Different standards and regulations require enterprises to use guards and protective devices (safeguards) on machinery.Unfortunately, some workers violate those regulations and remove guards or disable protective devices for different reasons.Therefore, they may end up having access to a hazard zone when a machine is operating.Such unsafe behavior is called bypassing.Bypassing of safeguards has been identified as a common OHS problem in most enterprises, and it is considered to be an international problem.As such, the International Social Security Association began an international project with the participation of Germany, Italy, Switzerland, and Austria as the project group to mitigate bypassing.This research proposes the prevention of bypassing safeguards and promoting the use of safeguards by designing, applying and improving upon a dedicated tool inspired by the ISO 14119 standard.This dissertation investigates three research questions: i) How can OHS practitioners identify the existing incentives to bypass on their machine?ii) How can OHS practitioners find preventive measures to overcome bypassing on their machine?iii) How can one assess those incentives in order to avoid bypassing safeguards of the machine during the use phase?This dissertation presents a list that covers a wide scope of 72 possible incentives to bypass based on the literature.They are categorized into five main categories: ergonomics, productivity,
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".