Anatomy Education Environment among Pre-Clinical Medical Students in Universiti Putra Malaysia Using Anatomy Education Environment Measurement Inventory

Anatomy is the foundation of medicine. Having adequate anatomy knowledge would improve medical students’ comprehension of pathology and patient management. The evolving scenario in anatomy education has created a changing educational environment in medical schools. Since educational environment influence the students’ motivation and ability to learn, it is pertinent to measure anatomy education environment as a feedback mechanism tool for further improvement in the curriculum. This study was performed to measure pre-clinical medical students’ perception of the anatomy education environment in Universiti Putra Malaysia (UPM) by using a validated 25-item inventory, known as the Anatomy Education Environment Measurement Inventory (AEEMI). The inventory was distributed online to 171 first- and second-year medical students to measure their perception of anatomy teachers and instructors, anatomy knowledge, their intrinsic interest and efforts in learning anatomy, anatomy learning resources and histology practical facilities. The analysis revealed that most of the items show “positive area” indicated by score of more than four. The first-year medical students showed a significantly higher perception of the anatomy education environment compared to the second-year medical students (p ≤ 0.05). However, both cohorts perceived an “area of improvement” for histology practical facilities (score of 3 to 3.99). In conclusion, the students were pleased with the anatomy education experience in UPM except for histology practical facilities that may require further improvement. The use of virtual microscopy in histology teaching would be a good alternative to overcome the problem in histology teaching in UPM, especially during the COVID-19 pandemic.

A Prototype Sensor System Using Fabricated Piezoelectric Braided Cord for Work-Environment Measurement during Work from Home

We proposed a new prototype sensor system to understand the workload of employees during telework. The goal of sensing using such a system is to index the degree of stress experienced by employees during work and recognize how to improve their work environment. Currently, to realize this, image processing technology with a Web camera is generally used for vital sign sensing. However, it creates a sense of discomfort at work because of a strong sense of surveillance. To truly evaluate a working environment, it is necessary that an employee be unaware of the sensor system and for the system to be as unobtrusive as possible. To overcome these practical barriers, we have developed a new removable piezoelectric sensor incorporated in a piezoelectric poly-L-lactic acid (PLLA) braided cord. This cord is soft and flexible, and it does not cause any discomfort when attached to the cushion cover sheet. Thus, it was possible to measure the workload of an employee working from home without the employee being aware of the presence of a sensor. Additionally, we developed a system for storing data in a cloud system. We succeeded in acquiring continuous long-term data on the vital signs of employees during telework using this system. The analysis of the data revealed a strong correlation between behavior and stress.

Anatomy Education Environment Measurement Inventory (AEEMI): a cross-validation study in Malaysian medical schools

Background The Anatomy Education Environment Measurement Inventory (AEEMI) evaluates the perception of medical students of educational climates with regard to teaching and learning anatomy. The study aimed to cross-validate the AEEMI, which was previously studied in a public medical school, and proposed a valid universal model of AEEMI across public and private medical schools in Malaysia. Methods The initial 11-factor and 132-item AEEMI was distributed to 1930 pre-clinical and clinical year medical students from 11 medical schools in Malaysia. The study examined the construct validity of the AEEMI using exploratory and confirmatory factor analyses. Results The best-fit model of AEEMI was achieved using 5 factors and 26 items (χ 2 = 3300.71 (df = 1680), P < 0.001, χ 2/df = 1.965, Root Mean Square of Error Approximation (RMSEA) = 0.018, Goodness-of-fit Index (GFI) = 0.929, Comparative Fit Index (CFI) = 0.962, Normed Fit Index (NFI) = 0.927, Tucker–Lewis Index (TLI) = 0.956) with Cronbach’s alpha values ranging from 0.621 to 0.927. Findings of the cross-validation across institutions and phases of medical training indicated that the AEEMI measures nearly the same constructs as the previously validated version with several modifications to the item placement within each factor. Conclusions These results confirmed that variability exists within factors of the anatomy education environment among institutions. Hence, with modifications to the internal structure, the proposed model of the AEEMI can be considered universally applicable in the Malaysian context and thus can be used as one of the tools for auditing and benchmarking the anatomy curriculum.

Anatomy Education Environment Measurement Inventory (AEEMI): A cross-validation study in Malaysian medical schools

Background: The Anatomy Education Environment Measurement Inventory (AEEMI) evaluates the perception of medical students of educational climates with regard to teaching and learning anatomy. The study aimed to cross-validate the AEEMI, which was previously studied in a public medical school, and proposed a valid universal model of AEEMI across public and private medical schools in Malaysia. Methods: The initial 11-factor and 132-item AEEMI was distributed to 1,930 pre-clinical and clinical year medical students from 11 medical schools in Malaysia. The study examined the construct validity of the AEEMI using exploratory and confirmatory factor analyses. Results: The best-fit model of AEEMI was achieved using 5 factors and 26 items (χ 2 = 3300.71 (df = 1680), P < 0.001, χ 2/df = 1.965, Root Mean Square of Error Approximation (RMSEA) = 0.018, Goodness-of-fit Index (GFI) = 0.929, Comparative Fit Index (CFI) = 0.962, Normed Fit Index (NFI) = 0.927, Tucker–Lewis Index (TLI) = 0.956) with Cronbach’s alpha values ranging from 0.621 to 0.927. Findings of the cross-validation across institutions and phases of medical training indicated that the AEEMI measures nearly the same constructs as the previously validated version with several modifications to the item placement within each factor. Conclusions: These results confirmed that variability exists within factors of the anatomy education environment among institutions. Hence, with modifications to the internal structure, the proposed model of the AEEMI can be considered universally applicable in the Malaysian context and thus can be used as one of the tools for auditing and benchmarking the anatomy curriculum.