A Student-Led Medical Education Initiative in Iran: Responding to COVID-19 in a Resource-Limited Setting
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».