A Student-Led Medical Education Initiative in Iran: Responding to COVID-19 in a Resource-Limited Setting
Bibliographic record
Abstract
To the Editor: Iran has had the highest number of COVID-19-related hospitalizations and deaths in the Eastern Mediterranean region: As of October 3, 2020, 464,596 confirmed COVID-19 patients and 26,567 deaths were reported. 1 When the first COVID-19 patient was detected in Iran on February 19, 2020, hospitals entered into a state of emergency due to shortages of personal protective equipment and frontline staff. Our medical school classes were suspended, and our clinical attending professors were overwhelmed with hospitals’ soaring patient loads. Additionally, limited infrastructure capabilities for transferring traditional in-person medical education to online platforms have contributed to anxiety and fear of an uncertain future amongst medical students. We, as senior medical students (sixth- and seventh-year students [medical interns]) in the capital city of Tehran, aimed to contribute to the COVID-19 response in Iran by filling the medical school educational gap through a student-led COVID-19 initiative. Under the supervision of 2 clinical attending professors, in late February we developed a student-led 2-week follow-up program for discharged COVID-19 patients. More than 70 fourth-year through seventh-year medical student volunteers participated in a 40-hour online training course on COVID-19-related prevention and care. Starting on March 9, 2020, through follow-up phone calls, fourth- and fifth-year medical students interviewed patients on days 1, 2, 3, 5, 7, 10, and 14 after their discharge using a predetermined research protocol; recorded patients’ clinical data in an online database; provided education and support for patients and their family members; and regularly reported patients’ status to senior medical interns and the 2 clinical professors. Patient profiles were presented in interactive online platforms (i.e., WhatsApp group, teleconference calls, Skype presentations) for a thorough discussion of lessons learned and improved decision making for future patient follow-ups. In the first phase of implementation, medical students collected data on more than 820 recovered COVID-19 patients via these telephone-based surveys. Our experience illustrates that medical students can play a meaningful and impactful role in the COVID-19 response via innovative online programs. Our student-led initiative has been well received and enhanced students’ learning processes by lowering cognitive distance, role modeling exercises, and providing a safe learning environment. Our program also helped address the COVID-19-related increasing levels of anxiety, frustration, fear, and demotivation among medical students and interns through regular meetings and engaging them in the COVID-19 response. Medical schools—particularly those in resource-limited settings with already overburdened health care systems and restricted financial and human capital resources—could greatly benefit from student-led, peer-to-peer online educational platforms designed to compensate for the loss of educational and direct patient care opportunities brought about by the COVID-19 pandemic.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".