METHODS: Data for this study were obtained from final year medical students' exit examination (n=185). Retrospective analysis of data was conducted using SPSS. Means for the six CSs assessed across the 16 stations were computed and compared.
RESULTS: Means for history taking, physical examination, communication skills, clinical reasoning skills (CRSs), procedural skills (PSs), and professionalism were 6.25±1.29, 6.39±1.36, 6.34±0.98, 5.86±0.99, 6.59±1.08, and 6.28±1.02, respectively. Repeated measures ANOVA showed there was a significant difference in the means of the six CSs assessed [F(2.980, 548.332)=20.253, p<0.001]. Pairwise multiple comparisons revealed significant differences between the means of the eight pairs of CSs assessed, at p<0.05.
CONCLUSIONS: CRSs appeared to be the weakest while PSs were the strongest, among the six CSs assessed. Students' unsatisfactory performance in CRS needs to be addressed as CRS is one of the core competencies in medical education and a critical skill to be acquired by medical students before entering the workplace. Despite its challenges, students must learn the skills of clinical reasoning, while clinical teachers should facilitate the clinical reasoning process and guide students' clinical reasoning development.
METHODS: This study employed a qualitative instrumental case study design intended to compare two groups of students-high-achieving students (n = 14) and low-achieving students (n = 5), enrolled in pre-clinical medical studies at the Universiti Malaya, Malaysia. Data were collected through reflective journals and semi-structured interviews. Regarding journaling, participants were required to recall their learning experiences of the previous academic year. Two analysts coded the data and then compared the codes of high- and low-achieving students. The third analyst reviewed the codes. Themes were identified iteratively, working towards comparing the learning processes of high- and low-achieving students.
RESULTS: Data analysis revealed four themes-motivation and expectation, study methods, self-management, and flexibility of mindset. First, high-achieving students were more motivated and had higher academic expectations than low-achieving students. Second, high-achieving students adopted study planning and deep learning approaches, whereas low-achieving students adopted superficial learning approaches. Third, in contrast to low-achieving students, high-achieving students exhibited better time management and studied consistently. Finally, high-achieving students proactively sought external support and made changes to overcome challenges. In contrast, low-achieving students were less resilient and tended to avoid challenges.
CONCLUSION: Based on the theory of action, high-achieving students utilize positive governing variables, whereas low-achieving students are driven by negative governing variables. Hence, governing variable-based remediation is needed to help low-achieving students interrogate the motives behind their actions and realign positive governing variables, actions, and intended outcomes.Key MessagesThis study found four themes describing the differences between high- and low-achieving pre-clinical medical students: motivation and expectation, study methods, self-management, and flexibility of mindset.Based on the theory of action approach, high-achieving pre-clinical medical students are fundamentally different from their low-achieving peers in terms of their governing variables, with the positive governing variables likely to have guided them to act in a manner beneficial to and facilitating desirable academic performance.Governing variable-based remediation may help students interrogate the motives of their actions.
AIMS: The aim of this study was to investigate Candida biofilm growth morphology, its biomass, metabolic activity, and to determine the effects of AbA on the biofilm growth.
METHODS: The biofilm forming ability of several clinical isolates of different Candida species from our culture collection was determined using established methods (crystal violet and XTT assays). The determination of AbA planktonic and biofilm MICs was performed based on a micro-broth dilution method. The anti-biofilm effect of AbA on Candida albicans was examined using field emission scanning electron microscope (FESEM) analysis.
RESULTS: A total of 35 (29.7%) of 118 Candida isolates were regarded as biofilm producers in this study. Candida parapsilosis was the largest producer, followed by Candida tropicalis and C. albicans. Two morphological variants of biofilms were identified in our isolates, with 48.6% of the isolates showing mainly yeast and pseudohyphae-like structures, while the remaining ones were predominantly filamentous forms. The biofilm producers were divided into two populations (low and high), based on the ability in producing biomass and their metabolic activity. Candida isolates with filamentous growth, higher biomass and metabolic activity showed lower AbA MIC50 (at least fourfold), compared to those exhibiting yeast morphology, and lower biomass and metabolic activity. The observation of filament detachment and the almost complete removal of biofilm from AbA-treated C. albicans biofilm in FESEM analysis suggests an anti-biofilm effect of AbA.
CONCLUSIONS: The variability in the growth characteristics of Candida biofilm cultures affects susceptibility to AbA, with higher susceptibility noted in biofilm cultures exhibiting filamentous form and high biomass/metabolic activity.