The risks of cannabis and other illicit drugs: Views among French and Finnish addiction treatment providers
Bibliographic record
Abstract
Simmat-Durand, L., & Koski-Jännes, A. (2015). The risks of cannabis and other illicit drugs: Views among French and Finnish addiction treatment providers. The International Journal Of Alcohol And Drug Research, 4(1), 61-69. doi:http://dx.doi.org/10.7895/ijadr.v4i1.201Aims: This study explores the effect of cultural context and group-level factors on the views held by treatment professionals in France and Finland about addiction and the dangers of illicit drugs.Design: Cross-cultural survey.Setting: Similar questionnaires were mailed to professionals working in specialized addiction treatment units in both countries.Participants: In Finland, 520 treatment providers working personally with clients responded, and 472 responded in France. The samples differed in several ways. Most notably, the medical profession was more dominant in France, while social work and counselling dominated in Finland.Measures: In addition to demographics, the questions covered different addictions, and included questions on the levels of danger of heroin, amphetamines and cannabis for individuals and the society.Findings: Consistent cultural differences appeared in the views of Finnish and French professionals regarding the addictiveness of illicit drugs and their level of danger to society. These differences remained significant after controlling for professions and other background variables.Conclusions: Cultural context, local prevalence of high-risk behaviors, familiarity with the substance, country of residence, and level of education appeared as major modifiers of risk perceptions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".