Presentation on theme: "Evidence-based drug policy – myth or reality? Alison Ritter, DPMP Director NDARC Presentation 6 th Feb, 2007, Canberra."— Presentation transcript:
Evidence-based drug policy – myth or reality? Alison Ritter, DPMP Director NDARC Presentation 6 th Feb, 2007, Canberra
Illicit drug policy Drug policy is complicated Multiple perspectives ―Users, families, health professionals, police, politicians, community members Strong public opinions Significant government spending (*) Complicated interventions (*)
Significant government spending Total spending: $3.2 billion p.a. Direct:$1.3 billion (41%) Indirect/consequences $1.9 billion (59%) Federal Govt:30% State/Territory Govt: 70% Law enforcement 56%
Government spending (direct only) Law enforcement $553.9m ($431 to 705) Interdiction $181.5m ($149 to 351) Prevention $295.8m ($88 to 534) Treatment $256.3m ($204 to 279) Harm reduction $ 26.3m ($19 to 44)
Complicated responses Law enforcement, eg: ∙Legalisation of drugs ∙Crop eradication programs ∙Customs and border control ∙Crackdowns and Raids ∙Police discretion, diversion, drug courts Prevention, eg: ∙Mass media campaigns ∙School-based drug education Treatment, eg: ∙Detoxification ∙Methadone or buprenorphine maintenance ∙Therapeutic communities ∙Cognitive behavioural relapse prevention Harm reduction, eg: ∙Needle Syringe Programs ∙Peer education for users ∙Non-injecting routes of administration
Evidence-based policy? Simple question = what works best? Research usually limited on this Doesn’t take into account dynamic interactions between sectors Doesn’t take into account different outcomes Doesn’t take into account policy making processes
Policy making processes - relationship to evidence? Uptake of evidence in policy-making Frustration by researchers Policy-makers feeling misunderstood Problems: ―long (researchers) vs short (policymakers) timeframes; ―ambiguity & lack of certainty in much social science research; ―inaccessibility of research results ―sheer bulk of research materials; ―research career structures and the academic reward systems; ―lack of clarity about roles (for example balancing objectivity and advocacy); ―rapid change in the policy environment; ―problems of governmental capacity; ―clash of cultures; and ―communication failures between researchers and policy makers
Solutions? ―summary reports, bulletins, dot points ―personalised briefings ―use of mail outs ―respect the limited time of policy makers ―be patient ―maintain a reputation of objectivity ―think about and prepare ‘good news’ angles to the research ―nurture political champions ―develop mutual understanding and respect But even with these, not much progress Solution may lie in understanding the policy- making processes better
“The policy world is as alien to most researchers as a distant foreign land and most do not even realise it” Michael Agar, 2002
Models of policy making There is not one model of how policy is made Researchers usually assume that the process is linear: Problem Options Solutions Implementation And that it is rational!
So, models of policy making… Technical/rational model Incrementalism model Power and pressure groups Interactive model Garbage can model Advocacy coalition framework Punctuated equilibrium etc
Rational/technical approach Conventional image: ID an issue, seek solutions Series of steps 1. identify problem 2. identify causes 3. develop options 4. analyse options 5. select an intervention 6. implement and evaluate Fundamental, exhaustive, rational, root approach
Rational/technical model Case example: improving pharmacotherapies - buprenorphine Implications for researchers ―Engage in steps 1-4 (ID problems, causes, options) ―Conduct research that is relevant, timely, credible ―Know which problems are on the agenda ―Have ready synthesised reports to feed into the problem, causes or options steps
Incrementalism Policy making is not dramatic – rather small incremental shifts Successive limited comparisons between existing policies (or alternatives) Comparing marginal values Better than to attempt (and fail) at big change Lindblom, C., E. (1959). The science of 'muddling through'. Public Administration Review, 19, 79-88. Lindblom, C., E. (1979). Still muddling, not yet through. Public Administration Review, 39(26), 517-526.
Incrementalism Case example: prevention programs in schools (education/information - competency approach) Implications for researchers ―Prepare for long time frame (tobacco 20+yrs) ―Tight simple comparative analyses (within budget) are highly valued.
“Garbage can” model Three independent streams: ―Problems ―Politics ―Policy processes/solutions, alternatives Sloshing around, waiting to be matched up Policy window opens: task = to match problems and solutions Kingdon, T. (2003) Agendas, Alternatives and Public Policies. (2nd Ed). NY: Longman
“Garbage can” model 1. PROBLEMS Agenda setting o Indicators and monitoring o Focusing events o Symbols o Budgets Interpretation Problem recognition (“should do something”) o Need a solution/alternative Rise and fade 2. POLITICS Agenda setting Influenced by: o National mood o Organised political forces o Governmental phenomena Consensus building through bargaining 3. POLICY PROCESSES Alternatives Policy community Ideas as an evolutionary processes (mutation & recombination) Criteria for success of an alternative (technical feasibility; values congruence; constraints manageable; public and political acceptability) Softening up (years) Emerging consensus (diffusion & tipping point) Coupling of 1 + 2 + 3 Policy entrepreneurs: join problem, solution and politics POLICY WINDOW Small/short and scarce. Predictable or Unpredictable “Problem” window (1) “Politics” window (2) Kingdon, T. (2003) Agendas, Alternatives and Public Policies. (2 nd Ed) NY: Longman
“Garbage can” model Case example: NCADA: problem = IDU and/or AIDS; politics = Hawke; policy processes/solutions = various (academics, drug treatment community, gay community). Implications for researchers ―“Policy processes” component – key role in presenting alternatives (and data on problems) ―Look for when policy windows open ―Match up problems and solutions creatively (don’t pair too early)
Power & pressure groups Three forces that determine policy: ―Ideology (philosophy, values) ―Interests (primarily self-interests) ―Information (multiple sources…) The distribution of power determines whose I-I-I will be dominant. Weiss, C. H. (1983). Ideology, interests and information: the basis of policy positions. In D. Callahan & B. Jennings (Eds.), Ethics, Social Sciences and Policy Analysis. NY: Plenum Press.
Power & pressure groups Case example: Diversion initiative ―Different constructions of the problem. Different Ideology, Interests and Information. Implications for researchers ―“Information” component ―Be aware of all “information” types and influences ―Strategic dissemination: mailouts, briefings etc.
Advocacy Coalition Framework Policy subsystem = interaction of diverse actors interested in same policy area. Illicit drugs as a policy subsystem. Within each policy subsystem, advocacy coalitions form (because diversity of views across the whole subsystem). Usually 2-4 AC’s. AC’s include: policy analysts, academics, journalists, advocates etc. Policy change occurs when AC’s are in conflict and one AC rises to ‘power’ – specifies the agenda, and the policies Sabatier, P. A. (1988). An advocacy coalition framework of policy change and the role of policy-oriented learning therein. Policy Sciences, 21, 129-168.
Advocacy Coalition Framework Case example: Supervised Injecting Centre (Van Beek, 2004) ―Players = local community, A&D service providers, local chamber of commerce, the churches, non-govt expert bodies, parliamentary processes, media, advocates. Implications for researchers ―Know the AC’s that exist ―Provide briefings etc for significant players ―Stakeholder engagement in the research from the start ―Use advocacy strategies
Summary Different models apply at different times Models overlap – they describe/focus on different components of the same processes No one way to ensure uptake of evidence
Don’t despair.. Role of evidence – in above models have mainly been looking at research as “instrumental” to a direct policy decision. ―Knowledge-driven (new science) ―Problem-solving (to answer a policy question) But other ways in which research evidence is used: ―Interactive (iteration among multiple players) ―Political (to support a position; “ammunition”) ―Tactical (to delay, deflect criticism, show responsibility) ―Enlightenment (new ideas permeate over time, “backdrop of ideas”) *
Where to from here? DPMP aims to develop the evidence-base for policy; develop, implementing and evaluating dynamic policy- relevant models of drug issues; and study policy-making processes in Australia Challenges Further work on models and what they mean for drug policy Comparisons of policy options Policy analysis rather than descriptive research Improving the evidence AND the intersection between researchers and decision-makers
Acknowledgements This work forms part of the Drug Policy Modelling Program (DPMP). Funded by: ―Colonial Foundation Trust ―NHMRC Career Development Award Thanks to: The DSS study group (at the ANU, led by Prof Bammer) RegNet, the ANU
Further information Assoc Prof Alison Ritter Drug Policy Modelling Program, Director National Drug and Alcohol Research Centre UNSW, Sydney, NSW, 2052, Australia E: firstname.lastname@example.org T: + 61 (2) 9385 0236 DPMP Monographs: http://notes.med.unsw.edu.au/ndarcweb.nsf Research – current – Drug Policy Modelling Program
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