Prevalence and Patterns of Polydrug Use in Latin America: Analysis of Population-based Surveys in Six Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The abuse of multiple substances continues to be a major public health concern in the United States, Latin America and other countries in the world. Recent studies have revealed that polydrug use has increased in many European countries. The main objective of this study was to determine the patterns of polydrug use in several Latin American countries. The data for this study was derived from separate studies conducted in Argentina, Bolivia, Chile, Ecuador, Uruguay and Perú. In each country a household survey was conducted using a multistage, stratified, cluster sample design. In all six countries, probabilistic samples of household residents aged 12 to 65 years of age were selected in three stages. The data were collected by a face to face interview using the same structured questionnaire, which was based on the Inter-American Uniform Drug Use Data System (SIDUC). A multivariate ordinal logistic regression model was fitted to assess the effects of country of origin on polydrug use, after adjusting for age and gender. The overall prevalence of polydrug use was 21%. The multivariate ordinal logistic regression model showed that males, participants aged 18 to 34 years and those from Chile, Uruguay and Argentina were significantly more likely to be polydrug users after adjusting for age and sex. This is the first study that documents the burden of polydrug use in Latin American countries. Future epidemiological studies should be conducted to examine the relationship between other demographic characteristics and risk behaviors with patterns of polydrug use.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it