Factors Affecting Paramedic Response Readiness to CBRN Threats in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To determine factors associated with increased response readiness to CBRN threats of paramedics in Ontario, Canada. METHODS: An internet-based survey was distributed via email and delivered at the start of each shift presentation during October, 2019. The target population was active-duty paramedics in the Ontario region of Canada. The survey was comprised of 6 sections pertaining to demographics, attitudinal components of risk perception, self-efficacy, deployment concerns, and resilience. Survey mean, univariate, and multivariate regression analyses were used to find the individual effect of each variable. RESULTS: The univariate analysis indicated that higher response readiness was associated with additional training, education, CBRN, and family concerns, and incident experience. However, some variables were non-significant in the multivariate analysis. Increased response readiness was associated with CBRN concerns and training. CONCLUSION: CBRN concerns and focused training regarding terrorism were both associated with increased response readiness. The information from the study can be used to build upon existing knowledge and support paramedics though training and preparation for CBRN specific disasters. The findings may also be used to improve current competency-based frameworks focused on response readiness.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".