PP44 Effect of COVID-19 on the operational realities of the non-urban Canadian paramedic
Bibliographic record
Abstract
Introduction COVID-19 has had significant impacts on the provision of pre-hospital care. Media and academic reports have heavily covered the impacts of Covid-19 on society and health care but have primarily focused on the densely packed urban environments which were hardest hit. This study examines the impact of COVID-19 on pre-hospital care outside of major urban centers. Methods The electronic patient care records (ePCR) of 3 Ontario paramedic services were compared for 365 days starting March 13 (start of lockdown for Ontario in 2020) for Historic (2018-2019), PreCOVID (2019-2020) and COVID (2020-2021) periods. Demographics were examined using t-Tests and proportion (Goodness of Fit) tests. Call counts in temporal categories were analysed using Chi-Squared tests of goodness of fit, while changes in the distribution of call time intervals were examined using a LR test for equivalence. Ethics for the study was approved by the Trent University Ethics Review Board. Results 89,126 ePCRs were examined (Historic: 30,070; PreCOVID: 30,576; COVID: 28,480). Small but statistically significant differences between COVID and PreCOVID were found in the gender and primary problem of patients (p < 0.01) as well as in all temporal fields (p < 0.03), except day-of-the-week (p = 0.87). In time interval comparison, time-on-scene and time-to-patient-contact were significantly increased in the COVID-19 era while time-to-offload (in the ER) was much shorter. Response times were found to be marginally slower, while transport-time-to-ED was found to be significantly faster during COVID. PreCOVID versus Historic comparisons found no significant difference, except in age and CTAS, which had significant differences in both the COVID vs PreCOVID and PreCOVID vs Historic periods (p < 0.001 for both). Conclusion COVID-19 has had many impacts on prehospital care. However, the practical significance of COVID on rural pre-hospital care may not be as large as that reported in higher density area.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.040 | 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".