Frontline Nursing Care: The COVID-19 Pandemic and the Brazilian Health System
Bibliographic record
Abstract
Emerging and reemerging infectious diseases are constant challenges for global public health. After the World Health Organization declared COVID-19 a pandemic on March 11, 2020, the spread of SARS-CoV-2 has been the focus of attention for scientists, governments and populations worldwide. In Brazil, the first case of COVID-19 was identified on February 26 2020, being the first country in Latin America to have affected patients. Almost four months later, more than one million confirmed cases of COVID-19 have been identified in the country, and the virus has spread across all 27 states and is responsible for at least 48,954 deaths until June 19, 2020. In addition, a global outbreak requires the active participation of the nursing workforce in clinical care, education, and sharing of accurate information of public health and policies. This year is particularly important for Nursing, as 2020 is the international year for Nursing and Midwifery Professionals. Nursing professionals corresponds to more than half of the health workforce in the country, being crucial in implementing public health policies and programs. Nurses and frontline health care workers have a critical role in the COVID-19 prevention and response, not only by providing direct assistance to patients and communities, but also in the implementation of health promotion and prevention strategies. Hence, we provide a reflection on the strengths and weaknesses of how the nursing profession is engaged with the COVID-19 response in Brazil.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".