A generic approach to developing human factors-quality assessment tools exemplified by the warehouse error prevention tool
Bibliographic record
Abstract
This study proposes a generic approach for creating human factors-based assessment tools to enhance operational system quality by reducing errors. The approach was driven by experiences and lessons learned in creating the warehouse error prevention (WEP) tool and other system engineering tools. The generic approach consists of 1) identifying tool objectives, 2) identifying system failure modes, 3) specifying design-related quality risk factors for each failure mode, 4) designing the tool, 5) conducting user evaluations, and 6) validating the tool. The WEP tool exemplifies this approach and identifies human factors related to design flaws associated with quality risk factors in warehouse operations. The WEP tool can be used at the initial stage of design or later for process improvement and training. While this process can be adapted for various contexts, further study is necessary to support the teams in creating tools to identify design-related human factors contributing to quality issues.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".