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What we Don’t Want to Know Keeps Hurting us

2002· article· en· W2021167287 on OpenAlexaboutno aff
Harald Klingemann

Bibliographic record

VenueAddiction · 2002
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsStalemateVettingNormativePolitical sciencePaymentGovernment (linguistics)Drug controlVisionPublic relationsScope (computer science)IdeologyLaw and economicsLawSociologyBusinessPoliticsComputer science

Abstract

fetched live from OpenAlex

The final report of the Committee on Data and Research for Policy on Illegal Drugs was confronted with the enormous challenge to ‘assess the knowledge available and needed to inform national drug control policy’. Consequently the scope of the committee’s work had to be limited and—given the controversial nature of the subject—the committee had to refrain from the normative task of recommending policy and staying out of ideological warfare. In the good tradition of empirical science, therefore, one expects from this report the unbiased outlining of policy options‚ possible scenarios and visions. Unfortunately, this expectation is rarely met. The introduction starts with a critical stance that the wars on drugs so far have cost the American taxpayer more than double the contributions to the payment for the Persian Gulf War in 1991 (Executive Summary, p. 1). While a forward-looking interdisciplinary analysis of drug policies and a fresh assessment of what works and how much it costs is certainly necessary at this point, the report does not sustain this style of inquiry. In a nutshell, it suggests ‘better methods and more data’ to overcome the current stalemate of American drug policy (‘to gather and interpret crucial data that are not available now’; ch. 1, p. 10). Even though ‘neutral science’ is promised, the committee considerably reduces the impact of its report because it deliberately excluded important debates and issues from its agenda: ‘we decided that changes in the legal status of addictive substances . . . are not under active consideration by policy makers for this committee to contemplate what data and research may have to say about these policy options’ (ch. 1, p. 6). This political credo goes along with an ethnocentric bias (or is it a national bias) and the implicit assumption that ‘what works elsewhere cannot work in America’. Except for the discussion of supply-orientated measures, such as crop-reduction policy, the international drug policy discussion is not taken up. Competing strategies and paradigms which at present are not politically correct in the United States are not addressed adequately in the report: harm reduction policies are practised successfully in many countries and have been subject to rigorous empirical evaluations (Bammer et al. 1999). Lack of discussion of the scientific assessments of heroin trials currently under way in many countries such as Spain, Germany and the Netherlands and of the routine practice of heroin-assisted treatment in Switzerland in its pursuit of a four-pillar drug strategy (prevention, repression, treatment, harm reduction) during the last decade exemplify this point (Ali et al. 1999). Even with a national expert report at hand, the exclusion of the international debate, including Canada (Fischer & Rehm 1997), and non-American data weakens the impact of the report unnecessarily. Furthermore, in spite of the mixed composition of the expert group, a ‘medical, clinical research model’ dominates the line of reasoning of this report. Chapter 8, ‘Treatment of drug users’, begins by stating that ‘only a minority of those who need drug treatment are currently receiving it’ (ch. 8, p. 2) and concedes that ‘permanent abstinence may not be a realistic goal of any single round of treatment for heavy long-term users’. Unfortunately, a discussion on controlled drug use and stepped care is not consequently taken up as one might expect. It is also acknowledged that the evaluation studies described in this section ‘only evaluated the self-selected group of patients who presented for treatment rather than the universe of sufferers in the community we would like to know the effects of treatment on all of those with the disorder, including those not presenting for treatment’ (ch. 8, p. 12). Hinting at the necessity of the ‘more powerful use of non-experimental observational studies’ (ch. 8, p. 4), the report raises hopes that it will provide support for more qualitative research, longitudinal studies and innovative methods to reach hidden populations by other means than traditional survey research; yet, on the contrary, the discussion focuses on clinical randomized controlled experiments. Available research on the natural history of addictions and ‘natural recovery or self-change’ (Robins 1993; Moos 1994; Sobell et al. 2000) is not presented even though it would provide important information on the barriers to treatment and highly cost-efficient minimal intervention and assisted ‘natural recovery’ (Klingemann et al. 2001). Again, the idea of spontaneous remission is not exactly popular. It challenges a deterministic disease concept and the idea of a return to controlled drug consumption from chronic use and has no place in the current political paradigm. The argument that ‘randomised trials quickly generate scientific consensus and often scientific consensus is needed if scientific evidence is to effect public policy’ (ch. 8, p. 4) may be valid for the medical scientific community but may be judged as, at best, naïve from a policy or political perspective. As already amply demonstrated in studies of other nations, the adoption and diffusion of treatment models is influenced by macro-societal factors and socio-political forces, with scientific findings often playing only a minor role (Klingemann & Klingemann 1999). The need for meta-analytical techniques and comparative studies is acknowledged in this report (ch. 8, p. 20) but applied only to the programme level when stating the necessity of ‘a growing understanding of the need to look for converging patterns across experiments’ (ch. 8, p. 20). This strategy needs to be complemented by the analysis of treatment systems on the national and international level (Bergmark 1999). Financing schemes, referral patterns, treatment philosophies, the dominating role of 12-Stepping within the American treatment system and the role of lay and self-help in general need to be better understood (Klingemann & Hunt 1998) before we can start trying to single out treatment effects and search in vain for ‘the silver bullet’.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.025
GPT teacher head0.267
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2002
Admission routes1
Has abstractyes

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