THE COVID-19 PANDEMIC AND PERSONAL SPENDING ON PROTECTION IN BRAZIL
Bibliographic record
Abstract
ABSTRACTThe two main questions addressed in this study are: What are the key drivers to spending on personal protective equipment (PPE) attributable to SARS-COV-2 risks? What are the welfare consequences of SARS-COV-2-induced changes in expenditures of personal individuals? We carried out an online survey conducted on internet and social media networks. We observed that the respondents most likely to spend a higher fraction of income on PPE expenditures are the ones who know someone who died from SARS-COV-2 and the ones who are married. Male respondents are less likely to purchase PPE. Individuals who can afford private health insurance are more likely to spend a higher proportion of income on PPE expenditures than others. Therefore, the higher the income and the number of deaths by SARS-COV-2 locally, the greater the amount spent with such purchases. The presence of children or other persons who need care in the respondent’s household affects positively as well. LA PANDEMIA DEL COVID-19 Y LOS GASTOS PERSONALES EN PROTECCIÓN EN BRASILRESUMENLas dos preguntas principales que se abordan en este estudio son: ¿cuáles son los factores clave para el gasto en equipo de protección personal (EPP) atribuibles a los riesgos del SARS-COV-2?, ¿cuáles son las consecuencias para el bienestar de los cambios inducidos por el SARS-COV-2 en los gastos de los individuos? Realizamos una encuesta en internet y redes sociales. Observamos que los encuestados con mayor probabilidad de gastar una fracción más alta de ingresos en EPP son los que conocen a alguien que murió de SARS-COV-2 y los que están casados. Es menos probable que los encuestados varones compren EPP. Las personas que pueden pagar un seguro médico privado tienen más probabilidades de gastar una mayor proporción en EPP que otros. Por lo tanto, cuanto mayor sea el ingreso y el número de muertes por SARS-COV-2 a nivel local, mayor será la cantidad gastada con dichos productos. La presencia de niños u otras personas que necesitan cuidados en el hogar del encuestado también afecta positivamente.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".