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
Bibliographic record
Abstract
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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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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".