A Short Intervention Followed by an Interactive E-Learning Module to Motivate Medical Students to Enlist as First Responders: Protocol for a Prospective Implementation Study
Notice bibliographique
Résumé
BACKGROUND: In Geneva, Switzerland, basic life support (BLS) maneuvers are provided in only 40% of out-of-hospital cardiac arrests (OHCAs) cases. As OHCA outcomes are markedly improved when BLS maneuvers are swiftly applied, a "first-responder" system was introduced in 2019. When emergency dispatchers identify a possible OHCA, first responders receive an alert message on a specific app (Save-a-Life) installed on their smartphones. Those nearest to the victim and immediately available are sent the exact location of the intervention. First-year medical students only have limited knowledge regarding BLS procedures but might nevertheless need to take care of OHCA victims. Medical students responding to out-of-hospital emergencies are off-duty in half of these situations, and offering junior medical students the opportunity to enlist as first responders might therefore not only improve OHCA outcomes but also foster a greater recognition of the role medical students can hold in our society. OBJECTIVE: Our aim is to determine whether providing first-year medical students with a short intervention followed by an interactive e-learning module can motivate them to enlist as first responders. METHODS: After obtaining the approval of the regional ethics committee and of the vice-dean for undergraduate education of the University of Geneva Faculty of Medicine (UGFM), 2 senior medical students will present the project to their first-year colleagues at the beginning of a lecture. First-year students will then be provided with a link to an interactive e-learning module which has been designed according to the Swiss Resuscitation Council's first aid guidelines. After answering a first questionnaire and completing the module, students will be able to register for practice sessions. Those attending and successfully completing these sessions will receive a training certificate which will enable them to enlist as first responders. The primary outcome will be the proportion of first-year medical students enlisting as first responders at the end of the study period. Secondary outcomes will be the proportion of first-year medical students electing to register on the platform, to begin the e-learning module, to complete the e-learning module, to register for practice sessions, to attend the practice sessions, and to obtain a certificate. The reasons given by medical students for refusing to participate will be analyzed. We will also assess how comfortable junior medical students would feel to be integrated into the first responders system at the end of the training program and whether it affects the registration rate. RESULTS: The regional ethics committee (Req-2020-01143) and the UGFM vice-dean for undergraduate education have given their approval to the realization of this study, which is scheduled to begin in January 2021. CONCLUSIONS: This study should determine whether a short intervention followed by an interactive e-learning module can motivate first-year medical students to enlist as first responders. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/24664.
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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 | 0,025 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,006 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,052 | 0,011 |
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 ».