Assessing human factors and ergonomics capability in organisations – the Human Factors Integration Toolset
Bibliographic record
Abstract
This paper presents the development of a tool that allows an organisation to assess its level of human factors (HF) and ergonomics integration and maturity within the organisation. The Human Factors Integration Toolset (available at: https://www.researchgate.net/project/Human-Factors-Integration-Toolset) has been developed and validated through a series of workshops with 45 participants from industry and academia and through industry partnered field-testing. HF maturity is assessed across five levels in 16 organisational functions based on any of 31 discrete elements contributing to HF. Summing element scores in a function determines a percent of ideal HF for the function. Industry stakeholders engaged in field-testing found the tool helped to establish the status of HF in the organisation, plan projects to further develop HF capabilities, and initiate discussions on HF for performance and well-being. Improvement suggestions included adding an IT function, refining the language for non-HF specialists, including knowledge work and creating a digital version to improve usability. Practitioner summary: A tool scoring HF capability in 16 organisation functions has been developed collaboratively. Industry stakeholders expressed a need for the tool and provided validation of tool design decisions. Field-testing improved tool usability and showed that beyond scoring HF capability, the tool created opportunities for discussions of HF-related improvement possibilities. Abbreviations: HF: human factors; HFIT: Human Factors Integration Toolset
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".