Perceptions of the population regarding generic drugs in Brazil: a nationwide survey
Bibliographic record
Abstract
BACKGROUND: Generic drugs (GDs) offer a way to reduce health spending without sacrificing quality. Despite this, there are doubts as to their acceptance by the population. This work aims to assess perceptions of GDs among the Brazilian population. METHODS: We conducted a national household survey face-to-face between April and May 2013, with 5000 individuals aged over 15 years. The questions explored socioeconomic and demographic characteristics, the use of GDs, and perceptions about GDs as compared to brand drugs (BDs). The chi-square test was used to examine the associations between the perceptions and the characteristics of the population. RESULTS: Of the 5000 participants, 51.3% were women, 40.2% were white, 48.6% were between 15 and 34 years of age, and 52.3% had income of less than two minimum wages (US$627.78). In terms of the use of GDs, 44.6% of the participants were taking or had taken GDs in the past three months, with the highest figures among the elderly (61.1%) and female (49.2%) populations. Regarding perceptions, 30.4% of the respondents considered GDs less effective than BDs; provided the same price, 59% would prefer BD, and 45.8% agreed that physicians prefer to prescribe GDs. The most negative perceptions about GDs were observed among lower income, elderly and nonwhite populations. CONCLUSION: The findings provide a better understanding of Brazilians' perceptions regarding GDs. This should be considered when formulating healthcare policies aiming at improving access to effective and quality drugs, and reduction of health costs.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".