A Controlled Experiment Investigating the Effects of Explanatory Manual on Adherence to Operating Procedures
Bibliographic record
Abstract
Operators’ adherence to operating procedures is a crucial factor for process safety in the process industry. Instruction manuals that document Standard Operating Procedures (SOP) are commonly used both as training materials and references during operation. Traditional SOP manual design emphasized using simple step-by-step instructions for how to do the tasks, but it often neglected the reasons why the steps and their specific orders should be closely followed. It is evident that operators sometimes choose to deviate from SOP intentionally if they do not understand the reasons and incorrectly deem the steps in the manual as slow or outdated. To help bridge the knowledge gap between SOP designers and operators, we advocate explanatory SOP manual design that adds the reasons for the steps in manual instructions. To examine the effect of explanatory manual, we conducted a controlled experiment using a hydraulic pump system that represented the wash operation in the electroplating industry. Participants’ performance and adherence to operating procedures (both Adherence to Production Order Procedures and Adherence to Wait Time) were measured and compared between the explanatory manual and the procedural manual conditions. The results showed that the explanatory manual had the benefit of increasing Adherence to Production Order Procedures, while time performance, Percent Duration within Bounds, and Adherence to Wait Time were not significantly affected. The finding supports the use of explanatory manuals because they have the potential to serve as an effective and economic way to improve operators’ adherence to operating procedures and process safety. Limitations of the laboratory setup were discussed.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".