The Chartered Institute of Ergonomics and Human Factors at 75: perspectives on contemporary challenges and future directions for Ergonomics and Human Factors
Bibliographic record
Abstract
As the UK’s Chartered Institute of Ergonomics and Human Factors (CIEHF) celebrates its 75th anniversary, it is worth reflecting on our discipline’s contribution, current state, and critical future endeavours. We present the perspectives of 18 EHF professionals who were asked to respond to five questions regarding the impact of EHF, contemporary challenges, and future directions. Co-authors were in agreement that EHF’s impact has been only limited to date and that critical issues require resolution, such as increasing the number of suitably qualified practitioners, resolving the research-practice gap, and increasing awareness of EHF and its benefits. Frequently discussed future directions include advanced emerging technologies such as artificial intelligence, the development of new EHF methods, and enhancing the quality and reach of education and training. The majority felt there will be a need for EHF in 75 years; however, many noted that our methods will need to adapt to meet new needs.Practitioner statement: This article provides the perspectives of 18 Ergonomics and Human Factors (EHF) professionals on the impact of EHF, contemporary challenges and critical future directions, and changes that are necessary to ensure EHF remains relevant in future. As such, it provides important guidance on future EHF research and practice.
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.
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 | 0.074 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".