Student-Run Online Journal Club Initiative During a Time of Crisis: Survey Study
Bibliographic record
Abstract
BACKGROUND: Since the closure of university campuses due to COVID-19 in spring 2020 necessitated a quick transition to online courses, medical students were isolated from hospitals and universities, negatively impacting their education. During this time, medical students had no opportunity to participate in academic discussions and were also socially isolated. Furthermore, medical doctors and professors of medical schools were given additional responsibilities during the pandemic because they were the frontliners in the fight against COVID-19. As a result, they did not have enough time to contribute effectively to medical student education. OBJECTIVE: This paper describes the establishment of the Cerrahpasa Neuroscience Society Journal Clubs, a group of entirely student-run online journal clubs at Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa. METHODS: The website, mass emailing, and social media accounts were used to announce the online journal clubs. Only medical students were eligible to apply. Journal clubs included psychiatry, neuroradiology, neurosurgery, neurology, and neuroscience. Following the last journal club meeting, a questionnaire created by the society's board was distributed to the participants. SPSS Statistics (version 26) was used for statistical analysis. RESULTS: Since March 15, 2021, synchronous online journal club meetings have been held every 2 weeks on a weekday using Google Meet, Microsoft Teams, or Zoom. Meetings of each journal club lasted approximately 1 hour on average. Interstudent interaction across multiple institutions was achieved since a total of 45 students from 11 different universities attended the meetings on a regular basis. Students on the society's board served as academic mentors for the clubs. The clubs received excellent feedback from participants, with an overall contentment score of 4.32 out of 5. CONCLUSIONS: By establishing these clubs, we have created a venue for academic discussions, which helps to reduce the negative impact of the pandemic on education. In addition, we believe it greatly aided students in staying in touch with their peers, thereby reducing the sense of isolation. We realize that traditional journal clubs are run by faculty; however, we believe that this experience demonstrated that medical students could run a journal club on their own since the feedback from participants was excellent. Additionally, as a medical student, being a journal club academic mentor is a challenging responsibility; however, having this responsibility significantly improved our academic mentors' leadership abilities.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".