Para Medicine Students’ Experiences of Virtual Education: A Qualitative Study
Bibliographic record
Abstract
Background: The emergence of the Internet and its development led to the expansion of virtual education. The development of virtual education in recent years has had a tremendous impact on the improvement of education and the establishment of educational justice in different parts of the world. Despite its advantages, virtual education is also associated with some challenges. The present study aimed to investigate para medicine students’ experiences of virtual education. Methods: This qualitative study was conducted using conventional content analysis. The participants were 25 students in various fields of para medicine who were selected using purposive sampling. The study took place at universities in the north of Iran. The data were collected using semi-structured in-depth interviews and focus groups from January to April 2022. The collected data were analyzed using Graneheim and Lundman’s qualitative content analysis method. Results: The core category identified in this study was from helplessness to interaction which was divided into three main categories (challenges and desperation, deprivation of mutual interaction and learning, and resilience and adaptation) and fourteen subcategories. Conclusion: The main theme extracted from the interviews with the participants was from helplessness to interaction. When a person has numerous failures, cannot control environmental conditions, and feels the ineffectiveness of their activity and response, they will experience a sense of vulnerability. Thus, the person has to accept the conditions and interact with them. In other words, interaction is a skill that improves the ability to quickly learn new skills and behaviors in response to the conditions. Thus, identifying challenges and realizing the weaknesses, strengths, opportunities, and threats governing the existing virtual education environment can help to turn many threats into opportunities, promote virtual education, and support teachers and students to improve the quality and quantity of the teaching-learning process in the COVID-19 and post-COVID-19 era.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".