Does Engineering Undergraduate Education Under-utilize Human Factors / Ergonomics?
Bibliographic record
Abstract
Human Factors or Ergonomics, (HFE) focuses on understanding interactions among humans and other elements of a system, to optimize human well-being and overall system performance. HFE is essential to ensuring engineers are prepared to meet ethical obligations for health, safety, welfare, environment protection, and cultural and societal sensitivity. Two studies measured exposure of graduates of accredited engineering programs to HFE content. Firstly, 37 engineering professors world-wide with knowledge of HFE teachings in their programs responded to a questionnaire. Most perceived HFE exposure as insufficient. Secondly, we searched for HFE keywords within required and technical elective course descriptions of 20 Canadian universities’ engineering programs. HFE keywords were absent from required courses for 68% of programs, and from elective course for 66% of programs; the highest required content was 7.5% of a program. We conclude that HFE is currently under-represented in engineering education to meet required professional obligations of engineers in Canada.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".