Despite the identification of crash hotspots as a first step of the roads safety management process, with various effective black spots identification (HSID) methods, only a few researchers have compared the performance of these methods; also it is not clear which test is the most consistent in the black-spots identification. In this research, seven commonly applied HSID methods (accident frequency (AF), PIARC coefficient based equivalent property damage only (EPDO), P-value (Islamic Republic of Iran Ministry Roads and Urban development), accident rate (AR), combined criteria, empirical Bayes (EB), societal risk-based) were compared against six robust and informative quantitative evaluation criteria (site consistency test, method consistency test, total rank differences test, total score test, sensitivity test and specificity test). These tests evaluate each method performance in a variety of areas, such as efficiency in identifying sites that show consistently poor safety performance, reliability in identifying the same black spots in subsequent time periods. To evaluate the HSID methods, three years of crash data from the Kerman state were used. Analytical Hierarchy Process (AHP) method has been used for determination the importance coefficients of evaluation tests and as a result, showed that the total rank differences test is the most appropriate test. The quantitative evaluation tests showed that the EB method performs better than the other HSID method. Test results highlight that the EB method is the most consistent and reliable method for identifying priority investigation locations. Overall, this result is consistent with the results of previous studies. The societal risk-based method performed worst in the all of the tests. It should be noted that advantages associated with the EB method were based on crash data from one of the road in Iran country, so the relative performances of HSID methods may change when using other crash data. However, the study results are consistent with earlier findings.

Generative artificial intelligence (GenAI) is emerging as a powerful tool in second language writing assessment, offering the potential for rapid, consistent, and scalable evaluation. However, its value depends on whether its scoring reflects the nuanced judgments of experienced human raters. This study introduces the concept of calibration in the context of second language writing assessment, defined as the deliberate and iterative refinement of AI prompts, guided by Statistical evidence, to align AI scoring with human evaluative reasoning. 60 essays produced by 30 upper intermediate learners of English were evaluated independently by an experienced human rater and by ChatGPT 3.5, using the English for Academic Purposes (EAP) Writing Assessment Rubric. Statistical analyses assessed inter rater agreement, score consistency, and systematic bias. In the initial baseline stage, ChatGPT 3.5 tended to act as a strict marker, applying the rubric literally and assigning lower scores than the human rater. Across three calibration stages, which included clarifying rubric descriptors, refining interpretive guidance, and incorporating representative scoring examples, the AI scoring moved closer to the human benchmark. Agreement improved from a Cohen’s kappa of 0.52 to 0.89, correlation from .76 to .94, and the mean score difference narrowed from -2.45 to - 0.95, the latter no longer Statistically significant. Qualitative analysis showed a shift from a narrow emphasis on surface errors to a more balanced consideration of accuracy, organization, development, and communicative effectiveness. The results suggest that calibration offers a replicable and evidence-based approach to integrating generative AI into second language writing assessment, enhancing the fairness, validity, and reliability of AI assisted evaluation.

Background: Students are a significant source of data for the evaluation of clinical instructors’ performance. This study was undertaken with the aim of adaptation and Validation of the Persian version of the Maastricht Clinical Teaching Questionnaire (MCTQ). The main objective of the researchers in this study was to evaluation the psychometric properties of the Persian version of the Maastricht Clinical Training Questionnaire (P-MCTQ) in Iran, considering cultural and social differences. Materials and Methods: This methodological study was conducted from 2019 to 2021 at four teaching hospitals affiliated with Sabzevar University of Medical Sciences, Iran. Qualitative and quantitative face and content validity, and construct validity methods were used for the validity evaluation. Stability and internal consistency methods were used, respectively, for the reliability evaluation of the questionnaire. Exploratory Factor Analysis (EFA) stage, 264 students studying in the fields of nursing, midwifery, anesthesiology, operating room, emergency medicine, and laboratory sciences completed the P-MCTQ. Results: The value of scale-content validity index (0. 92) is indicative of the overall content validity of the questionnaire. EFA extracted a single-factor structure that could explain the overall variance of the clinical education structure at about 76. 61%. The alfa and intraclass correlation values were equal to 0. 98 and 0. 82, respectively, indicating the excellent internal consistency and high overall stability of the questionnaire. Conclusions: The P-MCTQ is a valid and reliable tool for the evaluation of the teaching performance of clinical instructors.