The mediating role of mindfulness, attention and situational awareness on driving performance in a virtual reality underground mine
Bibliographic record
Abstract
Load-haul-dumps (LHDs) are used to transport materials in underground mining. Due to the design of LHDs and the design of the mine drifts, these vehicles are implicated in accidents involving other mining equipment, the mining environment and pedestrians. In 2015, the Ontario Ministry of Labour published the Mining Health, Safety and Prevention Review, which recommended that mobile equipment operators need to have a strong situational awareness. Mindfulness training can be used to improve an individual’s situational awareness and attention. Mindfulness is a trait that naturally varies amongst individuals. However, it is a technique that can be taught and with training and practice, a person’s mindfulness levels can improve over time. There has been limited research conducted in the area of mindfulness and workplace health and safety; however, there is evidence to suggest that mindfulness training may be a method to improve workplace safety. This study measured a person’s inherent mindfulness, attention and situational awareness and correlated them against driver’s performance measured from within a computer-based virtual reality underground mine simulator. The simulator, or the Situational Awareness Mining Simulator (SAMS), provided the virtual reality experience of operating an LHD in an underground mine. Perception-response time and collisions frequency were measured within the simulator and used as the measures of driver performance. Situational awareness was measured within the simulator by questioning the participants about physical aspects of the virtual mine, such as signage and colour of various objects. Mindfulness was measured using the Mindfulness Attention Awareness Scale (MAAS) and attention was measured using the Attention-Related Driving Errors Scale (ARDES-US). Participants (n = 21) operated a load-haul-dump in the simulator for two trials, each approximately 15-20 minutes in length. Spearman’s correlations showed a relationship between frequency of collisions and perception-response time (r = .449, p = .05); situational awareness and collision frequency (r = .507, p < .05); and situational awareness and mindfulness (r = .434, p < .05). These correlations were present in either Trial 1 or Trial 2, not both trials and thus, should be interpreted with caution. There was also a significant negative correlation between MAAS and ARDES-US scores (r = -.516, p = <.05). There were no other correlations present between ARDES-US scores and any other variables. This study provides evidence that by cueing individuals to aspects of their surroundings, Level 1 situational awareness (SA) can be increased and further, the relationship between SA and mindfulness becomes more apparent. No evidence was able to suggest a relationship between attention levels, as measured by ARDES-US and driving performance, or situational awareness. The learning curve of adapting to the simulator was substantial, and clouded some of the results, especially pertaining to collision frequency, and situational awareness.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".