Alternative Approaches to Clinical Practice in Medical Education During the Covid-19 Pandemic
Bibliographic record
Abstract
The aim of this study was to propose alternative approaches to clinical practice that would be effective substitutes for traditional clinical practice at the bedside of patients during a pandemic. For this purpose, an alternative approach and a method of determining the effectiveness of its use in medical educational institutions were developed through the method of synthesis. The level of medical competencies acquired during clinical practice was also assessed. The effectiveness of the proposed alternative approach is determined through the Pearson’s chi-squared test and Cohen’s coefficient. This study showed that the distance learning can be introduced through the methods that will allow medical students to acquire the clinical skills needed to perform their professional duties. In particular, video discussions of specific clinical cases, viewing videos of clinical procedures, interviews with a virtual patient, video conferencing, electronic testing, etc. are effective. They allow students to acquire the skills and abilities to conduct a survey of patients, their physical examination, prescription of additional examinations, interpretation of the results, performing clinical procedures, making medical records and more. The results of this study suggest that clinical practice can be realized remotely, if necessary. And the proposed alternative approach to clinical practice allows students to develop the necessary clinical competencies for future professional activities. This study revealed the need for further research to develop methods for assessing the clinical competencies of medical students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".