The mediating role of mindfulness, attention and situational awareness on driving performance in a virtual reality underground mine
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».