Faculty appointment and promotion in Taiwan's medical schools, a systematic analysis.

BACKGROUND: A rigorous faculty appointment and promotion (FAP) system is vital for the success of any academic institution. However, studies examining the FAP system in Asian universities are lacking. We surveyed the FAP policies of Taiwan's medical schools and identified an overreliance on the CJA score (manuscript Category, Journal quality, and Author order). The potential shortcomings of this metric and recommendations for refinement were discussed. METHODS: We obtained the FAP documents from all 12 medical schools in Taiwan, and analyzed their use of traditional versus non-traditional criteria for FAP according to a published methodology. The influence of the journal impact factor (JIF) on the FAP process was quantified by comparing its relative weight between papers with two extreme JIFs. To better understand the research impact and international standing of each school, we utilized the public bibliographic database to rank universities by the number of papers, and the proportions of papers within the top 10% or 50% citation. RESULTS: Compared with other countries, Taiwan's medical schools focus more on the quantifiable quality of the research, mostly using a "CJA" score that integrates the category, JIF or ranking, and authorship of a paper, with the JIF being the most influential factor. The CJA score for an article with a JIF of 20 can be up to three times the threshold for promotion to Assistant Professor. The emphasis on JIF is based on a presumed correlation between JIF and citation counts. However, our analysis shows that Taiwan's medical schools have lower-than-average citation counts despite a competitive rank in the number of publications. CONCLUSIONS: The JIF plays an unrivaled role in determining the outcome of FAP in Taiwan's medical schools, mostly via the CJA system. The questionable effectiveness of the current system in elevating the international standing of Taiwan's higher-education institutions calls for a re-examination of the FAP system. We recommend a reduction in the relative importance of CJA score in the FAP system, adopting more rigorous metrics such as the h-index for evaluating research quality, and supporting more research aimed at improving the FAP system.

Standard setting made easy: validating the Equal Z-score (EZ) method for setting cut-score for clinical examinations.

BACKGROUND: This study aims to assess the feasibility, reliability and validity of the panel-based Equal Z-score (EZ) method applied to objective structural clinical examination (OSCE) of Chinese medical students and undertaking a comparison with the statistical techniques-based Borderline Regression Method (BRM). METHODS: Data received from two cohorts of 6th and 7th year medical students in Taiwan who set the mock OSCE as a formative assessment. Traditionally this medical school uses BRM to set the pass/fail cut-score. For the current study, 31 OSCE panellists volunteered to participate in the EZ method in parallel to the BRM. RESULTS: In the conduct of this study, each panel completed this task for an OSCE exam comprising 12 stations within less than 60 min. Moreover, none of the 31 panellists, whose are busy clinicians, had indicated that the task was too difficult or too time-consuming. Although EZ method yielded higher cut-scores than the BRM it was found reliable. Intraclass correlation (ICC) measuring absolute agreement, across the three groups of panellists was .893 and .937 for the first and second rounds respectively, demonstrating high level of agreement across groups with the EZ method and the alignment between the BRM and the EZ method was visually observed. The paired t-test results identified smaller differences between the cut-scores within methods than across methods. CONCLUSIONS: Overall this study suggests that the EZ method is a feasible, reliable and valid standard setting method. The EZ method requires relatively little resources (takes about an hour to assess a 12 station OSCE); the calculation of the cut-score is simple and requires basic statistical skills; it is highly reliable even when only 10 panellists participate in the process; and its validity is supported by comparison to BRM. This study suggests that the EZ method is a feasible, reliable and valid standard setting method.

A meta-analysis for comparing effective teaching in clinical education.

Aim: Many factors affect learning outcomes, however studies comparing the effectiveness of different clinical teaching methods are limited. We utilize the list of influences on educational achievement compiled by John Hattie to inform a meta-analysis of learning effect sizes (ESs) associated with teaching-learning factors (TLFs) in clinical education. Methods: A literature search was conducted in PubMed to identify articles examining clinically relevant TLFs. Selection criteria were applied to identify learner-focused studies, with subsequent categorization by study design (pretest-posttest or controlled group). The Cohen's ES (d) for each TLF was extracted and a pooled ES determined. Results: From 3454 studies, 132 suitable articles enabled analysis of 16 TLFs' ESs. In general, ESs derived from pretest-posttest data were larger than those from controlled group designs, probably due to learner maturation effect. The TLFs of mastery learning, small group learning and goal settings possessed the largest ESs (d ≥ 0.8), while worked examples, play programs, questioning, concept mapping, meta-cognitive strategies, visual-perception programs and teaching strategies demonstrated ESs between 0.4 and 0.8. Conclusions: This is the first study to provide a rigorous and comprehensive overview of the effectiveness of TLFs in clinical education. We discuss the practical traits shared by effective TLFs which may assist teaching design.

Using machine learning to identify key subject categories predicting the pre-clerkship and clerkship performance: 8-year cohort study.

BACKGROUND: Medical students need to build a solid foundation of knowledge to become physicians. Clerkship is often considered the first transition point, and clerkship performance is essential for their development. We hope to identify subjects that could predict the clerkship performance, thus helping medical students learn more efficiently to achieve high clerkship performance. METHODS: This cohort study collected background and academic data from medical students who graduated between 2011 and 2019. Prediction models were developed by machine learning techniques to identify the affecting features in predicting the pre-clerkship performance and clerkship performance. Following serial processes of data collection, data preprocessing before machine learning, and techniques and performance of machine learning, different machine learning models were trained and validated using the 10-fold cross-validation method. RESULTS: Thirteen subjects from the pre-med stage and 10 subjects from the basic medical science stage with an area under the ROC curve (AUC) >0.7 for either pre-clerkship performance or clerkship performance were found. In each subject category, medical humanities and sociology in social science, chemistry, and physician scientist-related training in basic science, and pharmacology, immunology-microbiology, and histology in basic medical science have predictive abilities for clerkship performance above the top tertile. Using a machine learning technique based on random forest, the prediction model predicted clerkship performance with 95% accuracy and 88% AUC. CONCLUSION: Clerkship performance was predicted by selected subjects or combination of different subject categories in the pre-med and basic medical science stages. The demonstrated predictive ability of subjects or categories in the medical program may facilitate students' understanding of how these subjects or categories of the medical program relate to their performance in the clerkship to enhance their preparedness for the clerkship.

Development and validation of the student ratings in clinical teaching scale in Australia: a methodological study

PURPOSE: This study aimed to devise a valid measurement for assessing clinical students' perceptions of teaching practices. METHODS: A new tool was developed based on a meta-analysis encompassing effective clinical teaching-learning factors. Seventy-nine items were generated using a frequency (never to always) scale. The tool was applied to the University of New South Wales year 2, 3, and 6 medical students. Exploratory and confirmatory factor analysis (exploratory factor analysis [EFA] and confirmatory factor analysis [CFA], respectively) were conducted to establish the tool's construct validity and goodness of fit, and Cronbach's α was used for reliability. RESULTS: In total, 352 students (44.2%) completed the questionnaire. The EFA identified student-centered learning, problem-solving learning, self-directed learning, and visual technology (reliability, 0.77 to 0.89). CFA showed acceptable goodness of fit (chi-square P<0.01, comparative fit index=0.930 and Tucker-Lewis index=0.917, root mean square error of approximation=0.069, standardized root mean square residual=0.06). CONCLUSION: The established tool­Student Ratings in Clinical Teaching (STRICT)­is a valid and reliable tool that demonstrates how students perceive clinical teaching efficacy. STRICT measures the frequency of teaching practices to mitigate the biases of acquiescence and social desirability. Clinical teachers may use the tool to adapt their teaching practices with more active learning activities and to utilize visual technology to facilitate clinical learning efficacy. Clinical educators may apply STRICT to assess how these teaching practices are implemented in current clinical settings.

What impacts students' satisfaction the most from Medicine Student Experience Questionnaire in Australia: a validity study.

PURPOSE: This study evaluated the validity of student feedback derived from Medicine Student Experience Questionnaire (MedSEQ), as well as the predictors of students' satisfaction in the Medicine program. METHODS: Data from MedSEQ applying to the University of New South Wales Medicine program in 2017, 2019, and 2021 were analyzed. Confirmatory factor analysis (CFA) and Cronbach's α were used to assess the construct validity and reliability of MedSEQ respectively. Hierarchical multiple linear regressions were used to identify the factors that most impact students' overall satisfaction with the program. RESULTS: A total of 1,719 students (34.50%) responded to MedSEQ. CFA showed good fit indices (root mean square error of approximation=0.051; comparative fit index=0.939; chi-square/degrees of freedom=6.429). All factors yielded good (α>0.7) or very good (α>0.8) levels of reliability, except the "online resources" factor, which had acceptable reliability (α=0.687). A multiple linear regression model with only demographic characteristics explained 3.8% of the variance in students' overall satisfaction, whereas the model adding 8 domains from MedSEQ explained 40%, indicating that 36.2% of the variance was attributable to students' experience across the 8 domains. Three domains had the strongest impact on overall satisfaction: "being cared for," "satisfaction with teaching," and "satisfaction with assessment" (ß=0.327, 0.148, 0.148, respectively; all with P<0.001). CONCLUSION: MedSEQ has good construct validity and high reliability, reflecting students' satisfaction with the Medicine program. Key factors impacting students' satisfaction are the perception of being cared for, quality teaching irrespective of the mode of delivery and fair assessment tasks which enhance learning.

Factors predicting rural location employment intent and choice among medical students and graduates.

PURPOSE: Workforce shortage is a contributing cause of health inequality in rural Australia. There is inconclusive evidence demonstrating which factors cause doctors to choose rural practice. This study's objective is to determine predictive factors for medical students' intent to work rurally and for graduates' current rural employment location choice. METHODS: This prospective cohort study, utilized data gathered from the University of New South Wales about students and graduates who had spent one or more years in a Rural Clinical School. Participants were final year students and graduates already working in Australia. Stepwise logistic regression was used to determine predictive factors for the two outcomes. RESULTS: Predictors for student intent to work rurally are rural background (odds ratio [OR], 7.16; 95% confidence interval [CI], 2.59-19.53), choosing to study at the Rural Clinical School (OR, 8.72; 95% CI, 1.32-57.63), and perceiving rural areas as opportunistic for career advancement (OR, 1.69; 95% CI, 1.15-2.49). Predictors for graduates currently working in a rural location are Bonded Medical Program participation (OR, 6.40; 95% CI, 1.15-35.59) and personal altruism (OR, 1.91; 95% CI, 1.02-3.57). CONCLUSION: While intent is predicted by having a rural background, choosing to study at the Rural Clinical School and perception of rural areas as having positive career opportunities, a current rural workplace location among graduates is predicted by holding a bonded medical position and a desire to serve an under-resourced population. Maintaining the Bonded Medical Program and clear communication regarding training pathways may increase numbers of rural doctors.

Prediction of atrial fibrillation in patients with hypertension: A comprehensive comparison of office and ambulatory blood pressure measurements.

Hypertension is associated with the development of atrial fibrillation (AF). Evidence has shown that reverse dipping pattern, an abnormal increase of night-time blood pressure (BP) comparing to daytime BP, is associated with cardiovascular events. However, the relationship between diurnal changes in BP and AF has not been sufficiently explored. This paper aims to cross-sectionally explore the relationship between AF and ambulatory BP parameters, especially reverse dippers to the others, and further longitudinally analyze how BP patterns are associated to the risk of developing new-onset AF. Between February 2012 and March 2021, five out of 412 patients were identified of AF at baseline; four were reverse dippers (3.7%) and one was from the others (.3%). Cross-sectionally, the multivariate logistic regression analysis showed that reverse dippers were significantly more likely to have AF (odds ratio: 12.39, p = .030). After excluding patients with baseline AF, during the mean follow-up of 4.6 ± 3.0 years, seven patients developed AF. Longitudinally, the multivariate Cox regression analysis revealed that 24-h systolic BP (hazard ratio per 10 mmHg: 2.12, p = .015), night-time systolic BP (hazard ratio per 10 mmHg: 2.27, p = .002), and presentation of reverse dipping (hazard ratio: 5.25, p = .042) were independently associated with new-onset AF. None of the office BP measurements were associated with new-onset AF. While ambulatory BP measurements were better predictors for the incidence of AF, careful management is necessary for reverse dippers as they are at high risk of developing AF.

Equal Z standard-setting method to estimate the minimum number of panelists for a medical school's objective structured clinical examination in Taiwan: a simulation study

PURPOSE: Undertaking a standard-setting exercise is a common method for setting pass/fail cut scores for high-stakes examinations. The recently introduced equal Z standard-setting method (EZ method) has been found to be a valid and effective alternative for the commonly used Angoff and Hofstee methods and their variants. The current study aims to estimate the minimum number of panelists required for obtaining acceptable and reliable cut scores using the EZ method. METHODS: The primary data were extracted from 31 panelists who used the EZ method for setting cut scores for a 12-station of medical school's final objective structured clinical examination (OSCE) in Taiwan. For this study, a new data set composed of 1,000 random samples of different panel sizes, ranging from 5 to 25 panelists, was established and analyzed. Analysis of variance was performed to measure the differences in the cut scores set by the sampled groups, across all sizes within each station. RESULTS: On average, a panel of 10 experts or more yielded cut scores with confidence more than or equal to 90% and 15 experts yielded cut scores with confidence more than or equal to 95%. No significant differences in cut scores associated with panel size were identified for panels of 5 or more experts. CONCLUSION: The EZ method was found to be valid and feasible. Less than an hour was required for 12 panelists to assess 12 OSCE stations. Calculating the cut scores required only basic statistical skills.