Evaluation of Distance Anatomy Education in the Covid 19 Pandemic: A Survey Study
Bibliographic record
Abstract
Aim: The coronavirus disease, which emerged in the last quarter of 2019 in Wuhan, China, spread rapidly and adversely affected our daily lives and the world economy. In order cope with this adverse affect in education, the decision of distance education has been taken in our country and all over the world. The aim of this study was to investigate the views of medical students on effectiveness of the anatomy practical and theoretical courses given remotely due to the COVID 19 pandemic, and also to determine alternative social media and web-based programs to be used in distance anatomy education.Methods: One hundred and fifty-six students from the 1st and 2nd year of the Niğde Ömer Halisdemir University Faculty of Medicine participated in the study. The survey questions were uploaded to the Corporate Education Management and Planning System (KEYPS) and presented to the students to evaluate the effectiveness of practical and theoretical courses in anatomy.Results: While 20 (12.82%) of the participants stated that they did not agree with the proposition "Anatomy lessons are suitable for distance education" for theoretical lessons, and 48 (31.58%) stated that they did not agree with this proposal for practical lessons. On the other hand, the number of students who strongly agreed with this proposal for theoretical and practical sessions were 25 (16.03%) and 14 (9.21%) respectively. A total of 88 students who gave the answer "I strongly disagree or disagree" with the statement "Only using an anatomical model (model) is sufficient in asynchronous anatomy practical lessons", When these students were asked for alternative propositions, 79 (89.77%) of them preferred Digital cadaver applications and 80 (90.90%) preferred 3D Virtual Reality applications.Conclusions: As a result, when we look at the study data, it is seen that giving anatomy practical lessons from a distance is not effective for students, and it may be appropriate to teach theoretical lessons through distance education. However, students prefer social media and web-based programs as alternatives for anatomy theoretical and practical lessons in situations where education should be implemented remotely. In addition, we believe that this deficiency can be eliminated with Digital Cadaver Applications for cadaver education, which has an important place in practical anatomy courses, both in cases like pandemic where education is conducted remotely or in faculties that suffer from shortage of cadavers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".