Assessing human factors and ergonomics capability in organizations : The Human Factors Integration Toolset
Bibliographic record
Abstract
This paper presents the development of a tool that allows an organization to assess its level of human factors (HF) and ergonomics integration and maturity within the organization. The Human Factors Integration Toolset (available at: TBD) 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 organizational 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 organization, 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 organization functions has been developed collaboratively. Industry stakeholders expressed a need for the tool and provided validation of tool design decisions. Fieldtesting improved tool usability and showed that, beyond scoring HF capability, the tool created opportunities for discussions of HF-related improvement possibilities. Keywords: Macroergonomics, ergonomics strategy, organizational design and management, process management, operations management This paper was awarded a Liberty Mutual Award for 2020.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".