Nursing in Brazil: socioeconomic analysis with a focus on the racial composition
Bibliographic record
Abstract
OBJECTIVES: to analyze the socioeconomic characteristics of nurses and nursing technicians living in Brazil according to color/race. METHODS: based on the 2010 Demographic Census sample, 62,451 nursing professionals (nurses and technicians) living in Brazil were selected. Differences in monthly income were estimated by multivariate models, stratified by color or race groups (white, brown, and black). RESULTS: the majority were technicians (61.9%) of white color (54.3%). The income of white nurses exceeded that of brown and black nurses by more than a quarter; among technicians, white professionals had an income approximately 11% higher than brown and black nurses. CONCLUSIONS: differences between incomes of nursing workers were associated with ethnic/racial background, revealing situations in which white professionals systematically presented more favorable job and income conditions than black and brown professionals.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".