Acesso a medicamentos, o Sistema Único de Saúde e as injustiças interseccionais
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of general and public access to prescription drugs in the Brazilian population aged 15 or older in 2019, and to identify inequities in access, according to intersections of gender, color/race, socioeconomic level, and territory. METHODS: We analyzed data from the 2019 National Health Survey with respondents aged 15 years or older who had been prescribed a medication in a healthcare service in the two weeks prior to the interview (n = 19,819). The outcome variable was access to medicines, subdivided into general access (public, private and mixed), public access (via the Unified Health System - SUS) for those treated by the SUS, and public access (via the SUS) for those not treated by the SUS. The study's independent variables were used to represent axes of marginalization: gender, color/race, socioeconomic level, and territory. The prevalence of general and public access in the different groups analyzed was calculated and the association of the outcomes with the aforementioned axes was estimated with odds ratios (OR) using logistic regression models. RESULTS: There was a high prevalence of general access (84.9%), when all sources of access were considered, favoring more privileged segments of the population, such as men, white, and those of high socioeconomic status. When only the medicines prescribed in the SUS were considered, there was a low prevalence (30.4% access) that otherwise benefited marginalized population segments, such as women, black, and people from low socioeconomic backgrounds. CONCLUSIONS: Access to medicines through the SUS proves to be an instrument for combating intersectional inequities, lending credence to the idea that the SUS is an efficient public policy for promoting social justice.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".