Accuracy Of Performance Assessment Research Articles

Analisa Pengaruh Penggunaan Teknologi Dalam Manajemen Evaluasi Kinerja

Recent developments in information and communication technology such as artificial intelligence (AI) provide new opportunities to increase the efficiency and accuracy of performance assessment. The impact of the COVID-19 pandemic and the shift to a remote work model has created a need for technology solutions to unify and transmit the performance of employees working from multiple locations. The use of technology in performance management evaluations may raise concerns regarding data security and privacy, inconvenience or resistance to employees or stakeholders. Not all employees or managers have the same level of skill and understanding of the technology being implemented and it requires a high initial investment in maintenance and upgrades. This article provides an understanding of how technology can be applied in management performance evaluation and its benefits in increasing efficiency, objectivity and accuracy in employee assessment as well as providing recommendations for innovation and changes in the use of technology in management performance evaluation. The methodology used is a qualitative method by collecting data from various information sources. Some of the positive impacts of implementing technology in performance evaluation management are increased efficiency and productivity, real-time feedback, increased objectivity and fairness, more accurate and measurable performance monitoring. The technologies that can be used are Go Talent, Feedback and Recognition Systems, Analytics Technology, Application-Based Performance Evaluation Systems, Use of AI for Objective Evaluation and Chabots for Feedback.

Dynamic digging force modeling and comparative analysis of backhoe hydraulic excavators

The evaluation of excavator performance relies heavily on digging force, which serves as a crucial indicator. However, the accuracy of performance assessment is hindered by the absence of a suitable method to characterize the dynamic digging capacity of excavators. This study addresses this limitation by proposing an approach to establish a set of solution-limited inequalities for dynamic digging force. The approach incorporates D’Alembert’s principle and composite digging, while considering the influence of inertia force. Furthermore, to mitigate the issue of bucket tooth tip trajectory shaking caused by discontinuous posture during excavation, an amount of measurement data from a 20-ton machine is utilized to construct a consistent theoretical digging trajectory. The theoretical trajectory is subjected to numerical verification to determine the dynamic digging force along the trajectory. A comparative analysis is then conducted, contrasting the obtained dynamic digging force with different theoretical digging forces and measured resistances. Additionally, the dynamic digging forces within the selected digging area of the machine are characterized, without accounting for attitude continuity. The findings demonstrate that the dynamic digging force effectively captures the excavator’s performance along the trajectory, and it also provides an excellent characterization of the digging force at discrete digging spots.

Sources of Variability and Accuracy of Performance Assessment in the Clinical Pharmacology Quality Assurance Proficiency Testing Program for Antiretrovirals.

The Clinical Pharmacology Quality Assurance (CPQA) program provides semiannual proficiency testing (PT) of antiretroviral analytes for 11 US and international clinical pharmacology laboratories (CPLs) to ensure interlaboratory comparability. In this article, we provide estimates of the main sources of variability and assess the accuracy of the algorithm for the assessment of performance. Descriptive statistics are reported from 13 PT rounds from 2010 to 2016. Eight of the most common antiretroviral analytes were examined. Variance components analysis was used to rank the relative contributions of CPLs, antiretroviral analyte, and concentration category (low, medium, and high) to bias and variability using mixed models. Binary classification metrics of the PT assessment algorithm are calculated in comparison with a model using 95% prediction limits around estimated regression equations. CPLs provided 4109 reported concentrations of 65 unique samples for each of the 8 antiretroviral analytes across 13 PT rounds. Individual CPL accounted for the greatest amount of total variability (4.4%). Individual CPL and analyte combination (interaction) accounted for the greatest amount of bias (8.1%). Analyte alone accounted for 0.5% or less for total variability and bias. Overall, using a ±20% acceptance window around the final target, 97% of individual reported concentrations were scored acceptable, and 96% of antiretroviral/round scores were deemed satisfactory. Comparison with the regression model gave 100% sensitivity but only 34.47% specificity. Narrowing the acceptance window to ±15% improved specificity to 84.47% while maintaining a 99.17% sensitivity. The current CPQA PT scoring algorithm that use a ±20% acceptance window seems to suffer from a low specificity and may be too lenient. A stricter ±15% acceptance window would increase specificity and overall accuracy while lowering the overall pass rate by only 3%.