A cross-sectional survey of musculoskeletal disorder hazard exposures and self-reported discomfort among on-shore wind turbine service technicians
Bibliographic record
Abstract
BACKGROUND: Servicing and maintaining wind turbines may expose wind turbine technicians (wind techs) to musculoskeletal disorder (MSD) hazards. We aimed to characterise MSD hazard exposures and identify work elements that should be prioritised for MSD prevention efforts. METHODS: A cross-sectional online survey methodology gathered data from 144 wind techs based on a convenience, non-probability sampling approach. The survey was developed using resources from the Ontario MSD Prevention Guideline, where cognitive interviewing yielded wind tech specific modifications. RESULTS: Climbing was the most physically demanding task, followed by torqueing/tensioning and manual materials handling (MMH). However, working in awkward and constrained postures emerged as the task most likely to cause or aggravate discomfort. CONCLUSIONS: Injecting ergonomic and human factors principles into wind turbine design should be a high priority. Re-engineering tools like torque tensioning devices may reduce MSD hazard exposures associated with tasks including torqueing/tensioning and MMH. PRACTITIONER SUMMARY: We know little about musculoskeletal disorder (MSD) hazards associated with green jobs. By surveying wind turbine technicians, we learned that MSD hazards exists, and can be addressed by better considering human factors/ergonomics principles in the design of wind turbines and the tools required for service and maintenance operations. MSD: musculoskeletal disorder; MMH: manual materials handling; Wind techs: wind turbine technicians; CRE-MSD: centre of research expertise for the prevention of musculoskeletal disorders; CanWEA: Canadiarn wind energy association; HFE: human factors/ergonomics; ASME: American Society of Mechanical Engineers; CSA: Canadian Standards Association.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".