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 \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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».