Violence against paramedics: Protocol for evaluating one year of reports from a novel, point-of-event reporting process
Notice bibliographique
Résumé
BackgroundViolence against paramedics has been described as a “serious public health problem” with the potential for significant physical and psychological harm, but the organizational culture within the profession encourages paramedics to consider violence as just ‘part of the job’. The result is that most incidents of violence are never formally documented. This limits the ability of researchers and policymakers alike to develop strategies that mitigate the risk and enhance paramedic safety. ObjectivesFollowing the development and implementation of a novel, point-of-event violence reporting process in February 2021, our objectives are to: (1)Estimate the prevalence of violence and generate a descriptive profile for incidents of reported violence (2)Identify potentially high-risk service calls based on characteristics of calls that are generally known to the responding paramedics at the point of dispatch (3)Explore underpinning themes, including intolerance based on gender, race, and sexual orientation, that contribute to incidents of violence; and finally (4)Explore the potential contribution of frequent callers on the risk of violenceMethodsOur work is situated in a single paramedic service in Ontario, Canada. Using a convergent parallel mixed methods approach, we will retrospectively review one year of quantitative and qualitative data gathered from the External Violence Incident Reporting (EVIR) system. The EVIR is point-of-event reporting mechanism embedded in the electronic Patient Care Record (ePCR) developed through an extensive stakeholder engagement process. When completing an ePCR, paramedics are prompted to file an EVIR if they experienced violence on the call. Our methods include using descriptive statistics to estimate the prevalence of violence and describe the characteristics of reported incidents (Objective 1); logistic regression modelling to identify high-risk service calls (Objective 2) and the potential contribution of frequent callers on the risk of violence (Objective 4); and finally, qualitative content analysis of incident report narratives to identify underpinning themes that contribute to violence (Objective 3). ResultsWe anticipate being able to provide much needed epidemiological data on the prevalence of violence against paramedics in a single paramedic service, its contributing themes, and potential risk factors. ConclusionsOur findings will contribute to a growing body of literature demonstrating that violence against paramedics is a complex problem whose solutions require a nuanced understanding of its scope, risk factors, and contributing circumstances. Collectively, our research will inform larger, multi-site prospective studies already in the planning stage and inform organizational strategies to mitigate the risk of harm from violence.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».