Evidence map›Paper›PMID 22149758›Full record

ReviewThe American journal of drug and alcohol abuse2012

Monetary-based consequences for drug abstinence: methods of implementation and some considerations about the allocation of finances in substance abusers.

Jesse Dallery, Bethany Raiff

Abstract readReview
In one paragraph

Review in The American journal of drug and alcohol abuse, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jesse DalleryDepartment of Psychology, University of Florida, Gainesville, 32611, USA. dallery@ufl.edu
Bethany Raiff

Funding

Treatment Development & Evaluation CoreP30DA029926 · NIDA · DARTMOUTH COLLEGE · PI Lisa A. Marsch · 2011 to 2026
$21.5M
UF Clinical and Translational Science AwardUL1TR000064 · NCATS · UNIVERSITY OF FLORIDA · PI NELSON, DAVID R · 2012 to 2014
$12.2M
Technological Innovations in a Behavioral Treatment for Cigarette SmokingR01DA023469 · NIDA · NATIONAL DEVELOPMENT AND RESEARCH INSTITUTES, INC. · PI DALLERY, JESSE · 2009 to 2012
$1.3M
NCATS NIH HHS UL1 TR000064NIDA NIH HHS P30 DA029926NIDA NIH HHS R01 DA023469
6 · The paper itself

Abstract

unlabelledConceptualizing drug abuse within the framework of behavioral theories of choice highlights the relevance of environmental variables in shifting behavior away from drug-related purchases. Choosing to use drugs results in immediate and certain consequences (e.g., drug high and relief from withdrawal), whereas choosing abstinence typically results in delayed, and often uncertain, consequences (e.g., improved health, interpersonal relationships, money).

methodsThis is a selective review of the literature on Contingency management (CM).

resultsWe highlight a variety of methods to deliver CM in practical, effective, and sustainable ways. We consider a number of parameters that are critical to the success of monetary-based CM, and the role of the context in influencing CM's effects. To illustrate the broad range of applications of CM, we also review different methods for arranging contingencies to promote abstinence and other relevant behavior. Finally, we discuss some considerations about how drug-dependent individuals allocate their finances in the context of CM interventions.

conclusionsContingency management (CM) increases choice for drug abstinence via the availability of immediate, financial-based gains, contingent on objective evidence of abstinence.

Indexed as

Choice BehaviorDrug UsersHumansMotivationSubstance-Related Disorders

Identifiers

PMID22149758
PMCPMC3311913

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.