The health care emergency management experience during COVID-19: Lessons from a Pan-Canadian study
Bibliographic record
Abstract
The health care system in Canada has faced enormous pressures during the three consecutive waves of COVID-19 response. Without the efforts of emergency management practitioners, the health care system response would have been rendered less effective and less coordinated. This generational crisis not only challenged health systems, but had much \n \nbroader societal impacts, which will be experienced for years to come. In this article we distill findings from a Pan-Canadian study which speaks to the COVID-19 experience of emergency management practitioners in health care organizations. Based on these experiences, we present the practical implications for emergency management practitioners working in health. From this analysis, it becomes clear that emergency management will have an integral role in strengthening systems following the pandemic.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.034 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".