Preparing for the next COVID-19 wave in Canada: managing the crisis facing emergency management leaders in healthcare organisations
Why this work is in the frame
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Bibliographic record
Abstract
The ability of health system leaders to coordinate emergency responses to the novel coronavirus SARS-CoV-2 pandemic known as COVID-19 is a significant global issue. An effective response to emergencies in health organisations is predicated on the enactment of robust emergency management (EM) planning and activities. While these activities vary between countries, they share fundamentals that include the Hospital Emergency Incident Command System (HEICS), which is often led by the organisation’s chief executive. This incident command system has been used in the USA and other countries since 1991.1 Events such as the 1995 Tokyo Subway Sarin attack and the 2003 SARS outbreaks in Asia and Toronto, Canada, have transformed the requirements for hospital EM.1 While health emergency planning is widespread in the UK, it is not clear whether health organisations in that country are integrated into the emergency response, and whether they function effectively as a system.2 In the USA, several healthcare systems have attributed successful outcomes such as effective ventilator management to the implementation of HEICS.3–5 Meanwhile, in Canada, COVID-19 has tested these systems, and weaknesses are beginning to show in the capabilities of hospitals to provide a prolonged disaster response.6 Moreover, there is inconsistency across the Canadian provinces in the standardisation of incident command structures. The application of EM systems by Canadian healthcare leaders seems inconsistent and underused.7 8 Internationally, healthcare leadership (HL), those individuals in key positions of power whose decisions have considerable influence on emergency response activities, are not well integrated with EM systems and practices.2 The COVID-19 pandemic is a generational crisis that has significantly impacted the Canadian healthcare system. To date, the virus has not been contained, and while vaccinations have begun in Canada, future logistical and distribution challenges mean COVID-19 is still an ever-present concern. Globally, there …
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it