Ergonomic and usability ratings of helmets and head-mounted personal protective equipment in industry
Bibliographic record
Abstract
BACKGROUND: Anecdotal evidence from industry suggests that those working as arborists prefer to use minimal brim style, climbing helmets rather than traditional forestry helmets. In the mining industry, workers prefer wireless, LED cap lamps. Workers cite better comfort, better ability to see their work and better ventilation as reasons to use those helmets and cap lamps. Safety personnel in the industry would like to base future helmet decisions and requirements on a complete understanding of the ergonomic and safety issues of all available head-borne equipment. OBJECTIVE: Previous research has found that helmet design, head load and head/neck posture can influence the amount of neck discomfort experienced by users. Specific features of helmets and head-mounted personal protective equipment (PPE) in various industries have been changing to reflect ergonomic design principles. A series of three studies were conducted to evaluate usability and preference of new style cap lamps and helmet brims. PARTICIPANTS: PARTICIPANTS (n=10-16) were recruited primarily from undergraduate students, and each study represents a different group of novice participants. METHODS: Two different courses that included a tunnel were used in the first two studies to evaluate cap lamp styles and wireless cap lamps, while a simulated arborist task was used in the final study to evaluate helmet brim. Measures of ergonomic and discomfort questionnaires were analysed for this paper. RESULTS: The first cap lamp study was able to conclude that LED lamps are preferred over incandescent lamps, while the second study demonstrated that users prefer a multi-directional beam, and adjustability features of the cap lamp. In the final study, participants who must perform extreme overhead tasks prefer a helmet with a minimal brim. CONCLUSIONS: Additional research is warranted to determine whether actual, industry workers demonstrate the same preferences for these PPE items.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".