An Effective Prevention Program to Reduce Electrical Burn Injuries Caused by the Use of Multimeters
Bibliographic record
Abstract
The purpose of this study was to determine the circumstances of electrical burn injuries caused by the use of multimeters among electricians and electrical apprentices in Ontario and to develop a burn prevention program to reduce them. A survey to investigate electrical injuries caused by multimeters was mailed in June 2004 to 5000 Ontario electricians and electrical apprentices. A high voltage laboratory tested the effectiveness of fused leads to reduce multimeters malfunction. The results of the survey and laboratory tests helped to implement a burn prevention program. Then, a mail fused leads multimeter exchange program was implemented, and proposals to improve the multimeters standard were made to the Canadian Standards Association. Nine hundred (18%) workers responded the survey. There were 801 (89%) electricians, 81 (9%) electrical apprentices, and 27 (3%) with other qualifications. Ninety-nine (11%) had a multimeter fail during use, and half of them suffered critical burns. Causes of the injury were operator error (59%), wrong category rating (21%), defective equipment (18%), and others (2%). More than 2000 electrical contractors acquired the new fused leads multimeters. There were no critical injuries caused by multimeters in the years 2006, 2007, and 2008 (January to August) in Ontario. Understanding the cause of electrical burn injuries by multimeters and engaging members of the integrated electrical safety system in a multifaceted prevention program were effective in reducing electrical burn injuries. Fused leads multimeters proved to be effective in preventing most common user errors and electrical burn injuries caused by multimeters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".