ATTITUDES TOWARDS PEOPLE WITH PROBLEMATIC DRUG USE IN THE CITY OF LOJA, ECUADOR
Bibliographic record
Abstract
ABSTRACT Objective: analyze attitudes toward people with problematic drug use in the city of Loja, Ecuador. Method: a quantitative, cross-sectional, preliminary study using survey methodology aimed at collecting data on attitudes towards people with problematic drug use by residents in the city of Loja, Ecuador. The sample size is 121 individuals. This study is part of a multi-center investigation that involves 10 universities and a National Drug Council in Latin America. Results: the results revealed that attitudes toward people with problematic uses of alcohol are positive while attitudes toward people with problematic uses of other drugs such as marijuana and cocaine are ambivalent. It also shows that ambivalence prevails according to the majority of socio-demographic variables, differing in the masculine gender, people in the range of 18-29 years; those who lives with their partner; those who have a higher education than high school who have a positive attitude towards people with problematic uses of alcohol. And the scale that presents the greatest difficulties is the area of personal contact with negative attitudes towards the users of marijuana and cocaine. Conclusion: it is vital to continue researching about stigma, social distance and attitudes towards people who use drugs in the Ecuadorian context, its impact on treatment and social integration and the most appropriate information strategies to avoid stigma.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".