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 \ndesign of LHDs and the design of the mine drifts, these vehicles are implicated in accidents \ninvolving other mining equipment, the mining environment and pedestrians. In 2015, the Ontario \nMinistry of Labour published the Mining Health, Safety and Prevention Review, which \nrecommended that mobile equipment operators need to have a strong situational awareness. \nMindfulness training can be used to improve an individual’s situational awareness and attention. \nMindfulness is a trait that naturally varies amongst individuals. However, it is a technique that \ncan be taught and with training and practice, a person’s mindfulness levels can improve over \ntime. There has been limited research conducted in the area of mindfulness and workplace health \nand safety; however, there is evidence to suggest that mindfulness training may be a method to \nimprove workplace safety. \nThis study measured a person’s inherent mindfulness, attention and situational awareness and \ncorrelated them against driver’s performance measured from within a computer-based virtual \nreality underground mine simulator. The simulator, or the Situational Awareness Mining \nSimulator (SAMS), provided the virtual reality experience of operating an LHD in an \nunderground mine. Perception-response time and collisions frequency were measured within the \nsimulator and used as the measures of driver performance. Situational awareness was measured \nwithin the simulator by questioning the participants about physical aspects of the virtual mine, \nsuch as signage and colour of various objects. Mindfulness was measured using the Mindfulness \nAttention Awareness Scale (MAAS) and attention was measured using the Attention-Related \nDriving Errors Scale (ARDES-US). Participants (n = 21) operated a load-haul-dump in the simulator for two trials, each \napproximately 15-20 minutes in length. Spearman’s correlations showed a relationship between \nfrequency of collisions and perception-response time (r = .449, p = .05); situational awareness \nand collision frequency (r = .507, p < .05); and situational awareness and mindfulness (r = .434, \np < .05). These correlations were present in either Trial 1 or Trial 2, not both trials and thus, \nshould be interpreted with caution. There was also a significant negative correlation between \nMAAS and ARDES-US scores (r = -.516, p = <.05). There were no other correlations present \nbetween ARDES-US scores and any other variables. \nThis study provides evidence that by cueing individuals to aspects of their surroundings, Level 1 \nsituational awareness (SA) can be increased and further, the relationship between SA and \nmindfulness becomes more apparent. No evidence was able to suggest a relationship between \nattention levels, as measured by ARDES-US and driving performance, or situational awareness. \nThe learning curve of adapting to the simulator was substantial, and clouded some of the results, \nespecially pertaining to collision frequency, and situational awareness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".