Learning from public health and hospital resilience to the SARS-CoV-2 pandemic: protocol for a multiple case study (Brazil, Canada, China, France, Japan, and Mali)
Bibliographic record
Abstract
BACKGROUND: All prevention efforts currently being implemented for COVID-19 are aimed at reducing the burden on strained health systems and human resources. There has been little research conducted to understand how SARS-CoV-2 has affected health care systems and professionals in terms of their work. Finding effective ways to share the knowledge and insight between countries, including lessons learned, is paramount to the international containment and management of the COVID-19 pandemic. The aim of this project is to compare the pandemic response to COVID-19 in Brazil, Canada, China, France, Japan, and Mali. This comparison will be used to identify strengths and weaknesses in the response, including challenges for health professionals and health systems. METHODS: We will use a multiple case study approach with multiple levels of nested analysis. We have chosen these countries as they represent different continents and different stages of the pandemic. We will focus on several major hospitals and two public health interventions (contact tracing and testing). It will employ a multidisciplinary research approach that will use qualitative data through observations, document analysis, and interviews, as well as quantitative data based on disease surveillance data and other publicly available data. Given that the methodological approaches of the project will be largely qualitative, the ethical risks are minimal. For the quantitative component, the data being used will be made publicly available. DISCUSSION: We will deliver lessons learned based on a rigorous process and on strong evidence to enable operational-level insight for national and international stakeholders.
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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 | 0.115 | 0.092 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".