An ergonomics action research demonstration: integrating human factors into assembly 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
In action research (AR), the researcher participates 'in' the actions in an organisation, while simultaneously reflecting 'on' the actions to promote learning for both the organisation and the researchers. This paper demonstrates a longitudinal AR collaboration with an electronics manufacturing firm where the goal was to improve the organisation's ability to integrate human factors (HF) proactively into their design processes. During the three-year collaboration, all meetings, workshops, interviews and reflections were digitally recorded and qualitatively analysed to inform new 'actions'. By the end of the collaboration, HF tools with targets and sign-off by the HF specialist were integrated into several stages of the design process, and engineers were held accountable for meeting the HF targets. We conclude that the AR approach combined with targeting multiple initiatives at different stages of the design process helped the organisation find ways to integrate HF into their processes in a sustainable way. PRACTITIONER SUMMARY: Researchers acted as a catalyst to help integrate HF into the engineering design process in a sustainable way. This paper demonstrates how an AR approach can help achieve HF integration, the benefits of using a reflective stance and one method for reporting an AR study.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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