Peer Effects on Safety and Productivity in a Simulated Workplace: A Cross-Sectional Qualitative Usability and Validity Testing with University Students (Preprint)
Bibliographic record
Abstract
BACKGROUND: Peer effects influence workplace safety and productivity as employees observe and compare themselves to coworkers. However, prior simulation research has focused mainly on productivity in immersive settings, with limited attention to safety and little evidence on whether nonimmersive environments can capture peer effects across both domains. This study developed a nonimmersive virtual simulation to model peer effects on safety and productivity behavior. OBJECTIVE: This study aimed to assess the usability and ecological validity of a theoretically grounded nonimmersive virtual simulation designed to model peer effects through exposure to virtual coworker performance information. METHODS: A cross-sectional qualitative design was used across 2 iterative testing phases, alpha and beta. Participants were university students aged 18-30 years (alpha: n=12; beta: n=12). Alpha phase participants had prior kitchen experience to support early usability testing, while beta phase participants reflected the broader target population. The simulation involved timed meal preparation tasks with integrated safety requirements and peer performance feedback. Gameplay observations and semistructured interviews examined instructional clarity, environmental realism, virtual peer credibility, and scoring design. RESULTS: Alpha phase participants struggled with written instructions, perceived limited realism, and found peer comparisons ineffective because peer scores were unrealistic. After revisions, including experiential training, calibrated peer scores, clearer peer source messaging, and improved environmental cues, beta phase participants reported clearer instructions, greater realism, and more motivating peer comparisons. CONCLUSIONS: Results support that a theoretically grounded nonimmersive simulation refined through evidence-based feedback can elicit meaningful peer-related responses in safety and productivity contexts. By preserving calibrated social comparison, credible peer representation, and realistic task contingencies, the simulation offers a controlled and scalable way to examine how peer information shapes safety productivity dynamics. These findings expand methodological options for studying peer effects in settings where real-world experimentation may be impractical or unsafe.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".