Design of a Self-Audit Tool for the Application of Lockout on Machinery in the Province of Quebec, Canada to Control Hazardous Energies
Bibliographic record
Abstract
Failure to apply lockout procedures for the control of hazardous energies is one of the main causes of machinery-related fatal and serious injuries in North America. The absence of audits of lockout or the lack of proper tools for auditing lockout is prevalent, and thus the application of lockout is often not fully in compliance with standards and regulations. A self-audit tool for the application of lockout procedures for machinery was developed on the basis of the current standards and regulations, and previous research. The tool was then tested for content validity through experts’ opinions and qualitative feedback from six organizations in the province of Quebec in Canada. The developed audit tool defines the actual procedures to audit, as well as the surrounding conditions that are needed and the prerequisites based on standards, regulations, and findings from previous research. The results showed that the tool displayed a high content validity index and demonstrated that the usability, applicability, and comprehensiveness of the tool were adequate. This self-audit tool helps organizations monitor the application of lockout on machinery for the safety of workers and to ensure that the actual practice of controlling hazardous energy is in compliance with relevant standards and regulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".