Produção científica sobre a COVID-19 no Brasil: uma revisão de escopo
Bibliographic record
Abstract
Introduction: The national scientific production on COVID-19 has an immediate role in developing policies to tackle the disease and in guiding clinical decisions. Objective: To identify and characterize the scientific production on topics related to COVID-19 in Brazil in national journals from articles published between December 1, 2019, and May 2, 2020. Method: Scoping review, whose search for articles occurred in the SciELO Collection Brazil and on the websites of journal Visa em Debate and Ciência & Saúde Coletiva. The validated database was assessed by a simple quantitative analysis to provide numerical summaries of the characteristics of interest in the literature included in the review. Results: 58 (20.8%) articles from 22 national journals were included. The largest number of articles came from journals that developed fast publishing options or that had been adopting a continuous flow publication model (n = 45, 77.6%). The articles were framed in four categories, among seven defined: Comment (n = 43, 74.1%), Descriptive study (n = 8, 13.8%), Literature review (n = 6, 10.4 %) and Analytical study (n = 1, 1.7%). Only one systematic review was found and the analytical study was classified as an ecological study. April concentrated 86.2% of the articles published, with the peak of publications occurring on April 9 (8 articles). Among 58 articles, “Social isolation, mental health and other aspects related to social behaviours” was the most prevalent theme (n = 14, 24.1%). Conclusions: This scoping review produced a map of scientific production on COVID-19 in Brazil. There are important gaps, especially concerning randomized clinical trials and cohort studies, which need to be filled on further research in our country.
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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 | 0.029 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.024 | 0.027 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".