Quantifying an Organisation's Human Factors Capability: Targeting World Class Integration
Bibliographic record
Abstract
Many methods track company performance and process integration for quality, productivity, environment and safety. Similar methods do not exist for human factors (HF) even though it has impact on these outcomes. Without a HF specific assessment method it is impossible for managers to know if they are achieving 'world class' HF integration. An assessment tool is under development to address this need. The tool assesses the capability of each functional unit in an organization to manage HF aspects in their processes. This includes organizational strategy, design, maintenance, operations, logistics, marketing, and human resources, among others. For each department, the presence of HF aspects including indicators, process flows, and methods are evaluated. The maturity level of HF integration for each is rated in five classifications, conceptually similar to the Baldridge criteria, to reflect its level of 'world class'. The tool is non-prescriptive as it recognises the validity of different integration approaches. Progression to world class means HF works proactively becoming part of the organization's culture. With this tool companies can evaluate their ability to benefit from HF integration on an ongoing basis. It also provides a quantitative method for research and to benchmark macroergonomic capability in other organizations.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".