Violence against paramedics: Protocol for evaluating one year of reports from a novel, point-of-event reporting process
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".