The Association Between Preclinical Medical Students' Perceptions of the Anatomy Education Environment and Their Learning Approaches.

This study aimed to measure medical students' perceptions of the anatomy education environment and determine its association with their learning approaches. First- and second-year undergraduate medical students (N = 234) completed the Anatomy Education Environment Measurement Inventory and the Approaches and Study Skills Inventory for Students. Results revealed that the students' perceptions of all the factors in the anatomy education environment were positive except for two areas-the anatomy learning resources and quality of histology learning facilities. The majority of students in the first year (62.3%) and second year (43.7%) adopted a deep learning approach. There was no significant association between most of the factors in the anatomy education environment and the students' learning approaches, except for the students' intrinsic interest in learning anatomy factor (p = 0.032). The anatomy education environment was positive for the medical students, and there was a prevalence of a deep learning approach among them. However, most of the factors in the anatomy education environment did not influence the students' learning approach. Given that the students' learning approach is a multifactorial construct, investigating the relationship between these two variables in longitudinal time points would provide better insight into the association between the anatomy educational climate and students' learning approaches.

A Scoping Review of Effective Teaching Strategies in Surface Anatomy.

To become skilled physicians, medical students must master surface anatomy. However, the study of surface anatomy is less emphasized in medical and allied health science curricula, and the time devoted to direct engagement with the human body is limited. This scoping review was designed to answer one research question: "What are the elements and strategies that are effective in teaching surface anatomy?" The review was performed using a five-stage scoping review framework, including research question identification, relevant study identification, study selection, data charting, and result collating and reporting. Three databases were searched using two search terms combined with a Boolean operator: "teaching" and "surface anatomy." The initial pool of 3,294 sources was assessed for duplication, and study eligibility was evaluated using inclusion and exclusion criteria. Data were abstracted from 26 original articles by one researcher and verified by two other researchers. A thematic analysis was performed, and several elements of effective teaching strategies for surface anatomy were identified, namely contextualized teaching, embracing experiential learning, and learning facilitation. This review revealed that a multimodal approach was most commonly used in surface anatomy instruction. Hence, future research should explore the effectiveness of multimodal teaching strategies that adopt the three aforementioned primary elements of effective teaching in an authentic learning environment. It is pertinent to clarify the effectiveness of these teaching strategies by evaluating their impact on student learning, organizational changes, and benefits to other stakeholders.

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: A valid tool to measure the anatomy learning environment.

Students' perceptions of the education environment influence their learning. Ever since the major medical curriculum reform, anatomy education has undergone several changes in terms of its curriculum, teaching modalities, learning resources, and assessment methods. By measuring students' perceptions concerning anatomy education environment, valuable information can be obtained to facilitate improvements in teaching and learning. Hence, it is important to use a valid inventory that specifically measures attributes of the anatomy education environment. In this study, a new 11-factor, 132-items Anatomy Education Environment Measurement Inventory (AEEMI) was developed using Delphi technique and was validated in a Malaysian public medical school. The inventory was found to have satisfactory content evidence (scale-level content validity index [total] = 0.646); good response process evidence (scale-level face validity index [total] = 0.867); and acceptable to high internal consistency, with the Raykov composite reliability estimates of the six factors are in the range of 0.604-0.876. The best fit model of the AEEMI is achieved with six domains and 25 items (X2 = 415.67, P < 0.001, ChiSq/df = 1.63, RMSEA = 0.045, GFI = 0.905, CFI = 0.937, NFI = 0.854, TLI = 0.926). Hence, AEEMI was proven to have good psychometric properties, and thus could be used to measure the anatomy education environment in Malaysia. A concerted collaboration should be initiated toward developing a valid universal tool that, using the methods outlined in this study, measures the anatomy education environment across different institutions and countries. Anat Sci Educ 10: 423-432. © 2017 American Association of Anatomists.