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"Workload"
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Feasibility, tolerability, and preliminary effectiveness of a concurrent cognitive and physical training intervention in young healthy adults
[...]much of this research lacks the application of basic training principles such as individualisation and progressive overload, and consideration of the participant experience. [...]whether this novel training modality is being optimised and if it is feasible in the real-world is unclear. All participants randomised with baseline assessments were included in intention-to-treat analysis using linear mixed effects models. Considering physical performance, maximal oxygen uptake increased across the intervention with no difference between groups, as was also observed for the mean power of the physical training sessions.
Journal Article
Exploring the Relationship Between Workload, Leadership and Turnover Intentions among Taiwanese Army Personnel A Research Study
This study investigates the relationship between workload, leadership and turnover intention among personnel in the Taiwanese Army, with leadership as a mediator. The research subjects are personnel from the grassroots battalion units of the army, with a total of 415 valid responses collected. The collected data were statistically analyzed using descriptive analysis, reliability and validity analysis, discriminant validity analysis, correlation analysis, regression analysis, and Sobel test to understand the associations between workload, leadership and turnover intention. The findings reveal that workload has a significant positive impact on turnover intention, while it has a significant negative impact on leadership. Leadership in turn, exhibits a significant negative impact on turnover intention, with partial mediating effects. The emphasis of this research lies in fostering open communication between supervisors and subordinates, fair task allocation, adequate support and the establishment of a positive work environment and atmosphere to mitigate turnover intention among personnel. Subsequent recommendations are proposed for relevant military authorities and scholars to consider.
Journal Article
Life at the academic coalface: validation of a holistic academic workload estimation tool
This paper reports on research exploring the academic workload and performance practices of Australian universities. This research has identified a suite of activities associated with teaching, research and service, each with an associated time value (allocation). This led to the development of the academic workload estimation tool (AWET). In 2020, to validate the findings, we contacted academics willing to participate further and conducted interviews. We used the AWET to estimate workload for each individual for the previous year and compared it to the workload allocated according to their institutional workload model. Discrepancies were then discussed to ascertain to what extent the AWET was able to capture their work. In general, the participants thought the AWET provided a more realistic estimate of their actual work and highlighted how much is underestimated or unaccounted for by the workload models used within their institutions. It also showed how academic performance policies, focussed primarily on research output, disadvantaged many individuals because they ignored or minimised many scholarly, teaching and service-related tasks inherent in the academic role. Overall, the findings showed the AWET was a useful tool to discuss academic work and assisted them to better capture the complexity and extent of what they did. We offer the AWET as a validated approach for academics to estimate their workload in a holistic and transparent manner. We suggest its implementation institution-wide, along with an aligned performance policy, will facilitate negotiation of reasonable performance expectations. This will rebuild trust in the processes and improve a university’s effectiveness.
Journal Article
Self-Supervised Learning for Near-Wild Cognitive Workload Estimation
Feedback on cognitive workload may reduce decision-making mistakes. Machine learning-based models can produce feedback from physiological data such as electroencephalography (EEG) and electrocardiography (ECG). Supervised machine learning requires large training data sets that are (1) relevant and decontaminated and (2) carefully labeled for accurate approximation, a costly and tedious procedure. Commercial over-the-counter devices are low-cost resolutions for the real-time collection of physiological modalities. However, they produce significant artifacts when employed outside of laboratory settings, compromising machine learning accuracies. Additionally, the physiological modalities that most successfully machine-approximate cognitive workload in everyday settings are unknown. To address these challenges, a first-ever hybrid implementation of feature selection and self-supervised machine learning techniques is introduced. This model is employed on data collected outside controlled laboratory settings to (1) identify relevant physiological modalities to machine approximate six levels of cognitive-physical workloads from a seven-modality repository and (2) postulate limited labeling experiments and machine approximate mental-physical workloads using self-supervised learning techniques.
Journal Article