Ambulance Commanders’ Reluctance to Enter Road Tunnels in Simulated Incidents and the Effects of a Tunnel-Specific e-Learning Course on Decision-Making: Web-Based Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: The optimal response to a major incident in a road tunnel involves efficient decision-making among the responding emergency services (fire and rescue services, police, and ambulances). The infrequent occurrence of road tunnel incidents may entail unfamiliarity with the tunnel environment and lead to uncertain and inefficient decision-making among emergency services commanders. Ambulance commanders have requested tunnel-specific learning materials to improve their preparedness. OBJECTIVE: We aimed to assess decision-making among ambulance commanders in simulated road tunnel incidents after they had participated in a tunnel-specific e-learning course designed to support timely and correct decisions in this context. METHODS: We conducted a web-based intervention study involving 20 participants from emergency medical services in Sweden who were randomly allocated to a test or control group. The control group (n=10, 50%) received a lecture on general incident management, while the intervention group (n=10, 50%) completed an e-learning course consisting of 5 modules focused on tunnel structure, safety, and collaboration in response. The participants took part in 2 simulation-based assessments for ambulance commander decision-making in major road tunnel incidents 1 month and 6 months after their allocated study intervention. In each simulation, the participants decided on the best course of action at 15 independent decision points, designed as multiple-choice questions. The primary outcome was the correct response to the question regarding how to appropriately enter the road tunnel. The secondary outcome measurements were correct or incorrect responses and the time taken to decide for each of the 15 decisions. Limited in-depth follow-up interviews were conducted with participants (n=5, 25%), and collected data were analyzed using qualitative content analysis. RESULTS: All 20 participants completed the first simulation, and 16 (80%) completed the second. The main finding was that none (0/20, 0%) of the participants correctly answered the question on entering the tunnel system in the 1-month assessment. There were no significant differences between the groups (P=.59; 2-sample test of proportions) in the second assessment. The e-learning course was not associated with more correct answers at the first assessment, including accounting for participant factors (mean difference between groups: -0.58 points, 95% CI -1.88 to 0.73; P=.36). The e-learning course was also not associated with a shorter time to completion compared to the nonintervention group in either assessment. Interviews identified 3 categories linked to the main outcome: information (lack of), risk (limited knowledge and equipment), and mitigation (access to maps and aide-mémoire). CONCLUSIONS: Participation in a tunnel-specific e-learning course did not result in a measurable change in ambulance commanders' decision-making behavior during simulated road tunnel incidents. The observed hesitation to enter the road tunnel system may have several plausible causes, such as the lack of actionable intelligence and tunnel-specific plans. This novel approach to assessing commander decision-making may be transferable to other educational settings.
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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 | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 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 ».