Health equity and the usage of atypical antipsychotics within the Brazilian national health system: findings and implications
Bibliographic record
Abstract
BACKGROUND: There is a need to evaluate the health equity of atypical antipsychotics users who obtain their medicines from the Brazilian National Health System (SUS) through the identification of key factors that influence their health status due to concerns with equity of care. RESEARCH DESIGN AND METHODS: Cross-sectional study among patients attending state pharmacies in Brazil. Individuals were included if they used atypical antipsychotics, aged ≥18 years, and answered the EQ-5D-3 L questionnaire. Sociodemographic, behavioral, and clinical data were collected. The dependent variable was health status. Associations between the independent variables and the dependent variable were analyzed by adjusting a linear regression model. RESULTS: Overall, 388 individuals met the eligibility criteria and were included in the analysis. The final multiple linear regression model demonstrated a statistically significant association between VAS and suicide attempts, private care, current antipsychotics, comorbidities, and perceived family support. EXPERT COMMENTARY: The study identified several factors both individual and collective that correlate with the health status of atypical antipsychotic users and confirmed the importance of providing medicines for treating psychotic disorders. However, other factors are involved including social support. Our results suggest additional activities and policies are necessary including strategies to address the differences in private and public health care.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".