Design for human factors (DfHF): a grounded theory for integrating human factors into production design processes
Bibliographic record
Abstract
The 'design for human factors' grounded theory explains 'how' human factors (HF) went from a reactive, after-injury programme in safety, to being proactively integrated into each step of the production design process. In this longitudinal case study collaboration with engineers and HF Specialists in a large electronics manufacturer, qualitative data (e.g. meetings, interviews, observations and reflections) were analysed using a grounded theory methodology. The central tenet in the theory is that when HF Specialists acclimated to the engineering process, language and tools, and strategically aligned HF to the design and business goals of the organisation, HF became a means to improve business performance. This led to engineers 'pulling' HF Specialists onto their team. HF targets were adopted into engineering tools to communicate HF concerns quantitatively, drive continuous improvement, visibly demonstrate change and lead to benchmarking. Senior management held engineers accountable for HF as a key performance indicator, thus integrating HF into the production design process. Practitioner Summary: Research and practice lack explanations about how HF can be integrated early in design of production systems. This three-year case study and the theory derived demonstrate how ergonomists changed their focus to align with design and business goals to integrate HF into the design process.
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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 | 0.073 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".