Inquérito Telefônico de Fatores de Risco para Doenças Crônicas Não Transmissíveis em Tempos de Pandemia (Covitel): aspectos metodológicos
Bibliographic record
Abstract
This study describes the methodology of the Telephone Survey of Risk Factors for Chronic Noncommunicable Diseases During the Pandemic (Covitel), conducted in Brazil in 2022. Covitel is a population-based survey representing Brazil and its five macroregions (Central-West, Northeast, North, Southeast, and South) and providing information on the impact of the main risk factors for chronic noncommunicable diseases (NCDs) on the adult population aged 18 years or above who live in households served by fixed and mobile telephone lines. This study aims to contribute to the development and monitoring of public policies to promote the population’s health and obtain results to contribute to the knowledge of the influence of COVID-19 on risk factors for NCDs in the country. We evaluated 9,000 individuals and collected information on their diet, physical activity, mental health, health status, hypertension, diabetes, depression, and alcohol and tobacco consumption, comparing the pre-pandemic moments and the first quarter of 2022. We also collected information about the population’s vaccination schedule and COVID-19 infection history.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".