Perceptions of Addictions as Societal Problems in Canada, Sweden, Finland and St. Petersburg, Russia
Bibliographic record
Abstract
AIMS: This study reports on the relative gravity people attribute to various addictive behaviors with respect to other societal concerns in four northern populations with different history, social policy and treatment alternatives for addicted individuals. METHODS: Random population surveys were conducted in Canada, Sweden, Finland and St. Petersburg, Russia. In Finland and Sweden, the survey was conducted by mail, in Canada and St. Petersburg by phone. As a part of this survey, the respondents were asked to assess the gravity of various societal problems, some of which involved various addictive behaviors. The data were analyzed by descriptive statistical methods, factor analysis, contextual analysis and multiple regression analysis. RESULTS: Hard drugs, criminality and environmental issues belonged to the topmost problems in all data samples. Overall, Finns and Canadians appeared the least worried about various societal problems, Swedes seemed the most worried and St. Petersburgian views were the most polarized. Two factors were extracted from the combined data. Factor 1 covered criminal behavior and various addictions; it was named Threats to Safety factor. Factor 2 comprised social equality issues. The country context explained 12.5% of the variance of the safety factor and 7.9% of the equality factor. CONCLUSIONS: Despite some cultural variation in the gravity assessments, the central core of the social representation of addictive behaviors tends still to be linked with 'badness' since they were mainly grouped with various forms of criminal behavior in all these countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".