Design for human factors (DfHF): a grounded theory for integrating human factors into production design processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it