Produção científica sobre a COVID-19 no Brasil: uma revisão de escopo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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