Paramedic-reported infection prevention and control practices in Canadian paramedic services before and during COVID-19
Bibliographic record
Abstract
BACKGROUND: We aimed to characterize paramedic infection prevention and control (IPAC) measures reportedly used before and during the coronavirus disease 2019 (COVID-19) pandemic among select Canadian provinces. METHODS: IPAC measures were characterized using self-reported questionnaire data from paramedics enrolled in the CORSIP study between January 2021 and January 2023. Participant demographics and changes to IPAC measures were characterized using descriptive and nonparametric statistics. Cumulative IPAC changes were plotted against COVID-19 cases in the general population, denoting when paramedic-specific IPAC guidance would have been available. RESULTS: Significant variability was observed in worker demographics and which IPAC measures were reportedly used by paramedic services across Canada. Overall, paramedic participants (n=2,828) reported changes being made to all available IPAC measured soon after the pandemic was declared, primarily enhancement and new implementation of specific controls. Most paramedics reported using IPAC measures necessary when caring for COVID-19-infected patients. CONCLUSIONS: Paramedic services across Canada used variable IPAC measures before and during COVID-19. Although most employers were responsive in implementing changes early in the pandemic, validation of which controls are necessary for paramedic workers may lead to more consistent IPAC measures being used by paramedic services.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".