Evaluation of large-group lectures in medicine - development of the SETMED-L (Student Evaluation of Teaching in MEDical Lectures) questionnaire

Reimagining human dissection in preclinical medical education using studio-based learning: A retrospective pilot study

Human dissections in the current medical curriculum are conducted using a checklist approach to prioritize the exposure of anatomical structures. In this setting, anatomy educators are labored to enhance their engagement during the dissection. To address this issue, we considered the current medical education pedagogies and identified a novel approach of studio-based learning (SBL) for application in a Human Dissection Workshop. This study aimed to (1) evaluate students' perceptions of SBL, (2) appraise the impact of SBL on anatomical knowledge learning, and (3) interpret the results of a validated questionnaire. Workshop participants were recruited from Year 2 medical students at the Chinese University of Hong Kong from the 2020 and 2021 cohorts. Fifty-one students participated in the workshop (N = 24 [2020], N = 27 [2021]), and 50 of them completed the postworkshop questionnaire rated on a 5-point Likert scale. Nineteen items were validated using a factor analysis. The interpretation of the questionnaire results demonstrated the different learning outcomes of the workshop, which included (1) enhancing students' knowledge and spatial understanding of anatomical structures, (2) strengthening students' appreciation of gross pathologies and clinical relevance, and (3) promoting higher-order thinking skills. To our knowledge, this is the first study to introduce SBL in medical education. The successful implementation of the workshop reflects the promising potential of SBL for enhancing human dissection and supplementing the medical curriculum.

Position statement of the GMA committee "teaching evaluation"

The evaluation of teaching can be an essential driver for curriculum development. Instruments for teaching evaluation are not only used for the purpose of quality assurance but also in the context of medical education research. Therefore, they must meet the common requirements for reliability and validity. This position paper from the GMA Teaching Evaluation Committee discusses strategic and methodological aspects of evaluation in the context of undergraduate medical education and related courses; and formulates recommendations for the further development of evaluation. First, a four-step approach to the design and implementation of evaluations is presented, then methodological and practical aspects are discussed in more detail. The focus here is on target and confounding variables, survey instruments as well as aspects of implementation and data protection. Finally, possible consequences from evaluation data for the four dimensions of teaching quality (structural and procedural aspects, teachers and outcomes) are discussed.

A triangulation model for assessment of change in classroom behavior of medical teachers participating in faculty development program on lecturing skills

BACKGROUND & AIMS

We utilized a triangulation method of a faculty development program's (FDP) evaluation comprising short-course workshops on classroom behaviors and lecturing skills of basic sciences faculty in a medical school.

METHODS & RESULTS

This study utilized data from the pre and post evaluation of classroom lectures by an expert observer. Course participants were observed before the inception of a 4-month FDP and after 6-months of program completion. Findings at 6-month post-FDP interval were supplemented with students' and participant's self-evaluation. Expert evaluation of 15 participants showed that more participants were summarizing lectures at the end of their class (p = 0.021), utilizing more than one teaching tool (p  =  0.008) and showing a well-structured flow of information (p = 0.013). Among the students, majority (95.5%, n  =  728) agreed on "teachers were well-prepared for the lecture", however, a low number (66.1%, n  =  504) agreed on "teachers were able to make the lecture interesting". On self-evaluation (n =  12), majority of the participants (91.7%, n  =  11) thought these FDP workshops had a positive impact on their role as a teacher.

CONCLUSIONS

Gathering feedback from multiple sources can provide a more holistic insight into the impact of an FDP and can provide a robust framework for setting up future FDP targets.

Perception of health care students towards lectures as a teaching and learning method in the COVID era - A multicentric cross-sectional study from India

The sudden precipitation of the pandemic forced undergraduates to take refuge at home, deserting the campus. Consequently, the age-old classroom in person teaching-learning (T-L) method shifted and lessons had to be conducted online. In previous decades, archetypical classroom lectures survived a lot of criticism in the face of the quasi-passive nature of T-L  methodology. There are very few studies that reflect undergraduate students' perceptions of lectures. This study aimed to evaluate undergraduate students' perceptions of lectures using an online questionnaire with 13 items, which was circulated to undergraduate students of medical, physiotherapy, and nursing courses in three settings at different locations of private and public health schools. There was a total of 877 responses. The surveyed students were in favor of lectures and considered them indispensable for undergraduate learning. They preferred it as a kind of organized learning through the teacher's own experiences. Our study suggests that it is not the 'lecture' that requires mending but possibly teachers require better training, application of effective audio-visual aids, and innovative techniques to sustain students' interest in the class.

Perception of health care students towards lectures as a teaching and learning method in the COVID era - A multicentric cross-sectional study from India

The sudden precipitation of the pandemic forced undergraduates to take refuge at home, deserting the campus. Consequently, the age-old classroom in person teaching-learning (T-L) method shifted and lessons had to be conducted online. In previous decades, archetypical classroom lectures survived a lot of criticism in the face of the quasi-passive nature of T-L  methodology. There are very few studies that reflect undergraduate students' perceptions of lectures. This study aimed to evaluate undergraduate students' perceptions of lectures using an online questionnaire with 13 items, which was circulated to undergraduate students of medical, physiotherapy, and nursing courses in three settings at different locations of private and public health schools. There was a total of 877 responses. The surveyed students were in favor of lectures and considered them indispensable for undergraduate learning. They preferred it as a kind of organized learning through the teacher's own experiences. Our study suggests that it is not the 'lecture' that requires mending but possibly teachers require better training, application of effective audio-visual aids, and innovative techniques to sustain students' interest in the class.

Criteria for Teaching Performance in Psychology: Invariance According to Age, Sex, and Academic Stage of Peruvian Students

The use of scales to assess the performance of professors from the students' standpoint is a generalized practice in higher education systems worldwide. The purpose of this study is to analyze the factorial structure and measure the invariance of the Scale of Teaching Performance of the Psychology Professor (EDDPsic) among groups according to gender, age, and academic stage. The sample of participants was composed of 316 Psychology students from the fourth and sixth semesters (basic cycles), and from the eighth and tenth semesters (disciplinary-professional cycles) of two renowned public universities in Lima, Peru. Two hundred and thirty-one participants were women (73%), and the mean age of students was 21.5 years old (SD = 2.37). The measurement invariance of the scale in the three study variables was underpinned by a multigroup confirmatory factor analysis (MGCFA) conducted using a five-factor model that showed the best fitness indices. It is concluded that significant differences in measuring teaching performance areas of the professor depend on the students' age difference and on their academic stage (to attend the disciplinary-professional cycles).

Personalized dynamic evaluation technology of online education quality management based on artificial intelligence big data

Limited by the difficulty of management, the quality of online education is far worse than the quality of classroom teaching. In order to improve the effect of online education quality management, according to the actual needs of online teaching, this article builds personalized dynamic evaluation technology based on artificial intelligence big data technology and uses evaluation as the center of teaching. Each link in the teaching is for the generation and development of evaluation, and the teaching plan takes the learning objectives as the basis for the entire teaching process. Moreover, this research combines the needs analysis to build each functional module and build a model framework on this basis. In addition, in order to verify the performance of the model, this article conducts model analysis through practical teaching methods. The research results show that the model constructed in this paper has good performance.

[Quality management in a postgraduate refresher course in otolaryngology]

BACKGROUND

Residency training is often characterized by locally influenced training content and focus, which can lead to heterogeneous training outcomes. Refresher courses before the speciality certificate examinations can harmonize the situation.

OBJECTIVE

The current publication aims to present a quality management system for evaluation of a postgraduate refresher course for otolaryngology residents.

MATERIALS AND METHODS

The teaching sessions of a postgraduate course were evaluated using questionnaires. Descriptive statistics and multivariable binary logistic regression analysis were performed. To evaluate the factors leading to a negative perception of a teaching session, the focus was set on the worst 15% of all total ratings. An exemplary strength/weakness profile of a lecturer was created for individual feedback.

RESULTS

Analysis of the evaluation results showed an overall average rating of 12.8 (±2.4) out of a maximum of 15 possible points. Multivariable regression determined the items "friendliness," "systematic structure," "own involvement," "prior knowledge," and "efficient teaching session" to be significant for a negative perception of a teaching session. Using the lecturer profile, the strengths and weaknesses of the individual lecturer can be shown in an objective manner.

CONCLUSION

The developed questionnaire represents a good tool for quality management of a postgraduate refresher course for otolaryngology residents. This is achieved by regression analysis and creation of an individual lecturer profile, which provides an objective basis for improving the individual teaching session through detailed feedback to the lecturer.

Research on the development trend of foreign education based on machine learning and artificial intelligence simulation analysis

The development trend of foreign education is affected by many factors, so its future development trend is difficult to judge. Therefore, it is necessary to simulate and analyze the development trend of foreign education through artificial intelligence. According to actual needs, based on artificial intelligence algorithms, this paper builds artificial intelligence simulation analysis model to realize the simulation analysis of foreign education development. Moreover, starting from the overall design architecture of the online education platform, this paper builds functional modules, uses the machine learning constructed in this paper for data training and data prediction, and outputs prediction results. In order to study the performance reliability of the model, we predict and judge the development trend of foreign education and determine the model reliability through empirical judgment. The research results show that the model constructed in this paper has a certain effect.

The effective model of transformation of ideological and political education in universities based on artificial intelligence

In the context of artificial intelligence, the path of knowledge transmission needs to be transformed. In essence, the transmission of knowledge and the transformation of information transmission methods are integrated. This paper studies the foreign object tracking algorithm, analyzes the error in the target tracking algorithm, and uses the BP neural network principle to modify the IMM algorithm. Aiming at the problem of low tracking accuracy when the target is maneuvering, this paper analyzes the linearization error of Kalman filter and builds a BP neural network to correct the tracking model of IMM. The model creates a target prediction training set and a test set, optimizes the parameters of the neural network, and conducts simulation experiments using MATLAB, which proved that the model had a higher accuracy in predicting the target trajectory of foreign objects. Therefore, the transformation of ideological and political teaching mode in colleges and universities can be realized, and the intelligent classroom of ideological and political education and intelligent communication have technical support.

Research on the framework of university ideological and political education management system based on artificial intelligence

The importance of the management of ideological and political theory courses in colleges and universities is objective to the importance of ideological and political theory courses. At present, the management of ideological and political theory courses in colleges and universities has big problems in both macro and micro aspects. This paper combines artificial intelligence technology to build an intelligent management system for ideological and political education in colleges and universities based on artificial intelligence, and conducts classroom supervision through intelligent recognition of student status. The KNN outlier detection algorithm based on KD-Tree is proposed to extract the state information of class students. Through data simulation, it can be known that the KD-KNN outlier detection algorithm proposed in this paper significantly improves the efficiency of the algorithm while ensuring the accuracy of the KNN algorithm classification. Through experimental research, it can be seen that the construction of this system not only clarifies the direction of management from a macro perspective, but also reveals specific methods of management from a micro perspective, and to a certain extent effectively solves the problems in the management of ideological and political theory courses in colleges and universities.

Online teaching quality evaluation based on emotion recognition and improved AprioriTid algorithm

The association rule algorithm in data mining is used to study the factors that may affect students’ performance, to make suggestions for teaching work, and to provide decision-making basis for teachers and teaching administrators, which has practical significance. There are many potential applications for facial expression recognition technology. For example, in the teaching process, facial expression recognition technology helps teachers understand students and judge students’ reactions to certain things. Based on the current research status of emotion recognition and data mining algorithms, this paper improves the AprioriTid algorithm and constructs an online teaching quality evaluation model based on teaching needs. In addition, this article applies the model constructed in this article to the evaluation of English online teaching quality and evaluates teaching quality through data mining. The experimental research shows that the model constructed in this paper has good performance.

Role of machine learning and artificial intelligence algorithms for teaching reform of linguistics

The teaching of linguistics is limited by the influence of various factors, which leads to poor teaching effect, and the teaching process is difficult to evaluate. In order to improve the efficiency of linguistics teaching, this paper uses improved machine learning algorithms to construct a linguistics artificial intelligence teaching model. According to the teaching needs of linguistics, the efficiency of the teaching process is improved, and the teaching evaluation is performed, and the root cause analysis algorithm based on MCTS is optimized. Moreover, according to the frequent item set algorithm in data mining, a layered pruning strategy is proposed to further reduce the search space and improve the efficiency of the model. In addition, this study combines with the comparative teaching experiment to study the efficiency of artificial intelligence models in linguistics teaching. The statistical results show that the model proposed in this paper has a certain effect.

Diagnostic evaluation model of English learning based on machine learning

The diagnostic evaluation model of English learning is difficult to judge the subjective factors in student learning, so some diagnostic evaluation models of English learning are difficult to apply to English learning practice. In order to improve the effect of English learning, based on machine learning technology, this study combines the needs of English evaluation to build a diagnostic evaluation model of English learning based on machine learning. Moreover, this study compares the methods of random forest, Bayesian network, decision tree, perceptron, K-nearest neighbor and multi-model fusion, and selects the best algorithm for diagnostic analysis. The diagnostic evaluation model of English studies constructed in this paper mainly evaluates and judges the errors in students’ English learning. In addition, this study validates the methods proposed in this study through controlled experiments. The research results show that the method proposed in this study has a certain effect.

Research on ideological and political evaluation model of university students based on data mining artificial intelligence technology

The teaching evaluation index system based on artificial intelligence not only evaluates and reflects the teaching situation of ideological and political theory courses in universities as a whole, but also provides specific feasible goals and direction guidance for the construction of ideological and political theory courses in universities. Based on data mining technology, this paper combines machine learning algorithms and dimensional analysis to study the ideological and political evaluation model of colleges and universities and builds an artificial intelligence teaching evaluation model based on actual needs. Moreover, this study transforms the model selection problem into a hybrid optimization algorithm optimization problem, and the algorithm attempts to find the optimal model from the model set. In addition, this study designs a control experiment to perform model performance analysis. The results of the study show that the performance of the model meets the expected goals and can be applied to practice.

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

Performance analysis of public management teaching practice training based on artificial intelligence technology

At present, it is difficult to quantify the performance of public management teaching practice. Inviewof this, based on artificial intelligence technology, this study imitates the economic performance evaluation method to introduce evaluation parameters, and adopts the analogy method to introduce the artificial intelligence economic performance evaluation system into the model proposed in this study. Moreover, this study combines with actual teaching needs to build a performance analysis model of public management teaching training based on artificial intelligence technology. In addition, this study uses a B/S structure to build the system, set functional modules based on demand analysis, and use an expert system to score. In order to study system performance and system stability in the context of big data, the system performance is studied through actual scoring and large amounts of data training. The research results show that the model proposed in this paper has a certain effect.

Ideological and political teaching model using fuzzy analytic hierarchy process based on machine learning and artificial intelligence

Ideological and political education plays an important role in supporting social talent input. However, the current evaluation effect of ideological and political education is difficult to quantify. Therefore, in order to improve the evaluation effect of ideological and political education, based on artificial intelligence algorithms, this study combines machine learning ideas and the current status of ideological and political education to build a fuzzy analytic hierarchy process model of the of ideological and political teaching quality based on machine learning and artificial intelligence. Moreover, this study uses a three-tier structure to build a model network structure, and based on the characteristics of fuzzy evaluation, this study uses the expert system to conduct data management, operation and control of model evaluation, and build a corresponding database to update the data in real time. In addition, in order to verify the effect of the model, this study sets simulation experiments to analyze the model performance. From the point of view of running effect and running speed, this research model meets the actual needs of the system, so it can be applied to the evaluation process of ideological and political teaching quality in colleges and universities.

Big data based research on the management system framework of ideological and political education in colleges and universities

Artificial intelligence model combined with data mining technology can mine useful data from college ideological and political education management, and conduct process evaluation and teaching management. Therefore, based on the superiority of data mining technology and artificial intelligence system, this paper improves the traditional algorithm and constructs a university ideological and political education management model based on big data artificial intelligence. Moreover, this study uses a local sensitive hash function to generate representative point sets and uses the generated representative point sets for clustering operations. In order to verify the performance of the algorithm model, a control experiment is designed to compare the algorithm of this paper with traditional data mining methods. It can be seen from the research results that the algorithm model constructed in this paper has good performance and can be applied to practice.

An AI based design of student performance prediction and evaluation system in college physical education

College physical education is too one-sided, which makes the teaching process evaluation meaningless. Based on this, based on neural network technology, this article combines artificial intelligence teaching system to build an artificial intelligence sports teaching evaluation model based on neural network. The artificial intelligence model starts from the process evaluation and the final evaluation. Moreover, it uses a recurrent neural network for data training and analysis, and introduces a new decoder to perform data processing, and introduces a simplified gated neural network internal structure diagram to build the internal structure of the model.In addition, this study designs a control experiment to evaluate the performance of the model constructed in this study. The research results show that the artificial intelligence model constructed in this paper has a good effect in the performance prediction and evaluation of college sports students.

Online teaching quality evaluation model based on support vector machine and decision tree

At present, online education evaluation models are insufficient when dealing with small-scale evaluation data sets. In order to discriminate the learner’s learning state, this paper further studies online teaching machine learning methods, and introduces adaptive learning rate and momentum terms to improve the gradient descent method of BP neural network to improve the convergence rate of the model. Moreover, this study proposes a deep neural network model to deal with complex high-dimensional large-scale data set problems. In the process of supervised prediction, this study uses support vector regression as a predictor for supervised prediction, and this study maps complex non-linear relationships into high-dimensional space to achieve a linear relationship similar to low-dimensional space. In addition, in this study, small-scale teaching quality evaluation data sets and large-scale data sets are input into the model to perform experiments. Finally, the model proposed in this study is compared with other shallow models. The results show that the model proposed in this research is effective and advantageous in evaluating teaching quality in universities and processing large-scale data sets.

Evaluation model of classroom teaching quality based on improved RVM algorithm and knowledge recommendation

The intelligent evaluation of classroom teaching quality is one of the development directions of modern education. At present, some teaching quality evaluation models have accuracy problems, and the evaluation process is affected by a variety of interference factors, which leads to inaccurate model results, and it is impossible to find out the specific factors that affect teaching. In order to improve the accuracy of classroom teaching quality evaluation, this study improves RVM based on the method of feature extraction and empirical modal decomposition of ACLLMD method, and establishes classroom theoretical teaching quality evaluation model and experimental teaching quality evaluation model based on RVM algorithm. Moreover, this study uses test data to analyze the accuracy and reliability of the evaluation results to verify the feasibility and reliability of the new method. In addition, this study verifies the reliability of this algorithm by comparing with the manual scoring results. The research results show that RVM can be used to construct classroom theory teaching quality evaluation models and experimental teaching quality evaluation models with high accuracy and good reliability.

Smart teaching evaluation model using weighted naive bayes algorithm

There is a certain subjectivity in the teaching evaluation process, which leads to a low accuracy of the intelligent scoring system. In order to promote the intelligent development of teaching evaluation, based on machine learning, this study briefly introduces the background and current status of teaching evaluation, and describes in detail the relevant algorithm principles of data analysis and modeling using data mining technology and machine learning methods. Moreover, this study describes the establishment process of the traditional classroom teaching evaluation system and uses the classification algorithm in machine learning in the construction of evaluation models to further improve the scientificity and feasibility of teaching evaluation. In addition, in this study, empirical algorithm is used as the basic algorithm to evaluate teaching quality, and the topic word distribution obtained by joint model training is used as the original knowledge. Finally, this research analyzes the performance of this research system through a control experiment. The research results show that the scores of the research model are close to the standard manual scores and can provide a theoretical reference for subsequent related research.

Nonparticipant Student Observation of Faculty Classroom Teaching

Student evaluations are commonly used to evaluate teaching effectiveness. Nonparticipant observation uses individuals who are not a part of the learning process but rather are observers who can formulate observations about the teaching encounter, possibly with less bias than student evaluators. The purpose of this article is to analyze reports by inexperienced nonparticipant observers of faculty classroom teaching episodes. The hypothesis was that veterinary faculty have common characteristics in their classroom teaching that are observable by nonparticipant observers and that these are similar to characteristics observed historically by student evaluators. This study is a qualitative document analysis of written observations made by senior veterinary students attending pre-clinical classroom lectures by a faculty member. Each written report was analyzed using thematic concept analysis, and the researchers met multiple times throughout the process to discuss the analysis and develop conclusions about themes that were encountered consistently among observations. Common emergent themes included information formats, PowerPoint presentations, timing, organization, student engagement, and delivery. Nonparticipant observers may contribute valuable data that may enhance faculty development in pedagogy. Observations may serve to augment data from student evaluations, self-reflection, and peer assessment.

Psychometric properties of the Persian version of bedside teaching (BST) Instrument

INTRODUCTION

Bedside teaching plays a crucial role in acquiring essential clinical skills. Therefore, the main aim of this study is assessing the validity and reliability of the Persian version of German bedside teaching (BST) instrument. This instrument was specially developed for evaluation of bedside teaching.

METHOD

The present cross-sectional study was conducted on 150 last year medical students, using convenience sampling. The Persian version of the bedside teaching (BST) was used for data gathering. To calculate the reliability of the questions, Cronbach's alpha was used and to determine the construct validity of the questionnaire, confirmatory factor analysis was used. All analyses were performed in LISREL 10 and SPSS 21 software.

RESULTS

Cronbach's alpha indicated excellent reliability for each subscale (α =0.77-0.85). All of the value of the questions are more than a significant number of 1.96 and concluded to be significant. There was an acceptable fit between the hypothetical model and the data and all comparative fit indices (CFI, NFI, RFI, IFI) showed good model fitness. BST is a valid and reliable instrument for the assessment of clinical teaching at bedside. It has 18 items with 5 point Likert scales.

CONCLUSION

The findings suggest that the Persian version of the BST questionnaire is a valid and reliable tool for the evaluation of teachers and providing feedback in a clinical setting. However, more studies should be conducted in other cities in Iran.

Presentation and evaluation of the teaching concept "ENHANCE" for basic sciences in medical education

A solid understanding of basic sciences is a prerequisite for successful completion of medical education. Therefore, it is essential to improve the quality of teaching and to ensure the applicability of basic sciences. Based on practical experiences and previous research, we developed an innovative step-by-step concept, called ENHANCE, for the implementation or revision of teaching units, especially for basic sciences. We used comparative self-assessment gains, a questionnaire to assess teaching quality as well as end-of-semester evaluations (students' satisfaction and open-ended questions) to evaluate the ENHANCE concept. It was found that ENHANCE-based teaching units were related to increased students' satisfaction, high attendance rates and that restructuring the course curriculum yielded in a positive assessment of teaching effectiveness. The revised courses were rated as the very best of all classes in several semesters. Qualitative data showed that students particularly appreciated the level of comprehension and how helpful the courses were for the understanding and preparation of the regular curriculum.

Association between leniency of anesthesiologists when evaluating certified registered nurse anesthetists and when evaluating didactic lectures

Daily evaluations of certified registered nurse anesthetists' (CRNAs') work habits by anesthesiologists should be adjusted for rater leniency. The current study tested the hypothesis that there is a pairwise association by rater between leniencies of evaluations of CRNAs' daily work habits and of didactic lectures. The historical cohorts were anesthesiologists' evaluations over 53 months of CRNAs' daily work habits and 65 months of didactic lectures by visiting professors and faculty. The binary endpoints were the Likert scale scores for all 6 and 10 items, respectively, equaling the maximums of 5 for all items, or not. Mixed effects logistic regression estimated the odds of each ratee performing above or below average adjusted for rater leniency. Bivariate errors in variables least squares linear regression estimated the association between the leniency of the anesthesiologists' evaluations of work habits and didactic lectures. There were 29/107 (27%) raters who were more severe in their evaluations of CRNAs' work habits than other anesthesiologists (two-sided P < 0.01); 34/107 (32%) raters were more lenient. When evaluating lectures, 3/81 (4%) raters were more severe and 8/81 (10%) more lenient. Among the 67 anesthesiologists rating both, leniency (or severity) for work habits was not associated with that for lectures (P = 0.90, unitless slope between logits 0.02, 95% confidence interval -0.34 to 0.30). Rater leniency is of large magnitude when making daily clinical evaluations, even when using a valid and psychometrically reliable instrument. Rater leniency was context dependent, not solely a reflection of raters' personality or rating style.