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Governance of Addictions: European Public Policies Governance of Addictions: European Public Policies

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Date added: 09/17/2014
Date modified: 09/17/2014
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Tamyko Ysa, Joan Colom, Adrià Albareda, Anna Ramon, Marina Carrión & Lidia Segura

This, the first in the series of books from ALICE RAP published by Oxford University Press, aims to contribute to the understanding of addiction policies in Europe. There are many governance studies by substances, but no aggregate study looking at how addictions, as a whole, are governed. Evidence from 28 countries is studied in relation to 4 substances (alcohol, tobacco, cannabis and heroin), allowing each country to be placed on 2 scales according to the structure (from comprehensive lifestyle policy to substance specific reactive policies) and strategy (from individual security to societal wellbeing outcomes) that addictions governance followed.
The result is the clustering of countries into 4 groups representing different governance models which have been labeled:

  • Traditional approach: where the most important aim is to reduce supply through criminal justice measures – the reason for the security and reduction emphasis in these countries is that they have historically been entrance points or routes for illicit drugs into the EU.
  • Transitioning: treatment is more important than punishment, but these countries still tend not to lean towards decriminalisation in policies.
  • Regulation of legal substances: Emphasis is on preventing heavy use over time. These countries adopt a wellbeing approach and focus on scales that are evidence-based.
  • Trendsetters in illicit substances: Countries here adopt a well-being and comprehensive approach (dealing with multiple substances together). The focus is on harm-reduction for illegal substances and countries in this group rank low in regulation of legal substances – alcohol and tobacco.

There is a tendency in EU for countries to move from traditional to trendsetting models. The overall aim is to take the best for addictions governance from each model.