The engineer and the physician: can they understand each other when they talk about machine vibration?
Bibliographic record
Abstract
This reflexive study, based on an analysis of university curricula in Quebec in the fields of occupational medicine and mechanical engineering, as well as on a qualitative analysis of a questionnaire sent to representatives of universities, aims to understand whether the physician and the engineer can speak the same language about the prevention of worker exposure to the vibrations produced by machines and/or industrial equipment. Exchanges between the two disciplines are rare and even nonexistent. What knowledge must be shared by the engineer and the physician regarding the design of machines or their field evaluation concerning prevention? What do engineers know about ergonomics, biomechanics, and the physical hazards of machines? What do occupational physicians know about modal analysis, tribology, or even machine vibration, all relevant themes in the field of vibration expertise? Is knowledge sharing or a language common to both disciplines necessary? How can occupational physicians understand the models that engineers create to reproduce the harmful effects of vibration on workers? Does the engineer have solutions to respond to the concerns of occupational physicians regarding the safety of machines and equipment, in the presence of a worker with vibration syndrome or low back pain caused by whole-body vibration exposure? Can the occupational physician provide his assistance to the engineer's concerns? The challenges of occupational physicians and engineers do not seem to dovetail. More specific ergonomic knowledge in the university curricula of both disciplines could possibly be the beginning of a common basis for the work of engineers and occupational physicians.
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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 | 0.039 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| 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".