Evidence map›Paper›PMID 26084667›Full record

ReviewCurrent psychiatry reports2015

Understanding and shifting drug-related decisions: contributions of automatic decision-making processes.

Kenneth M Carpenter, Gillinder Bedi, Nehal P Vadhan

Abstract readReview
In one paragraph

Review in Current psychiatry reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Kenneth M CarpenterDepartment of Psychiatry, Columbia University Medical Center, Center for Motivation and Change, Division on Substance Abuse, New York State Psychiatric Institute, 1051 Riverside Drive, MC 120, New York, NY, 10032, USA, carpent@nyspi.columbia.edu.
Gillinder Bedi
Nehal P Vadhan

Funding

A Sequenced Behavioral and Medication Intervention for Cocaine DependenceR01DA034087 · NIDA · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI CARPENTER, KENNETH MICHAEL, LEVIN, FRANCES RUDNICK · 2013 to 2018
$3.5M
Neurobehavioral Mechanisms of Decisions to Smoke Marijuana and Cocaine in HumansK23DA034877 · NIDA · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI BEDI, GILLINDER I. · 2013 to 2017
$930k
NIDA NIH HHS K23 DA034877NIDA NIH HHS R01 DA034087
6 · The paper itself

Abstract

While substance use is common, only a minority of individuals who use drugs or alcohol develop problematic use. An understanding of the factors underlying the transition from substance use to misuse may improve prevention and intervention efforts. A key feature of substance misuse is ongoing decisions to use drugs or alcohol despite escalating negative consequences. Research findings highlight the importance of both relatively automatic, associative cognitive processes and relatively controlled, deliberative, and rational-analytic cognitive processes, for understanding situational decisions to use drugs. In this review, we discuss several cognitive component processes that may contribute to decision-making that promotes substance use and misuse, with a focus on more automatic processes. A growing body of evidence indicates that relative differences in the strength of these component processes can account for individual differences in the transition from substance use to misuse and may offer important avenues for developing novel intervention strategies.

Indexed as

Decision MakingAttentionCognitionHumansImpulsive BehaviorInteroceptionPsychotherapySubstance-Related Disorders

Identifiers

PMID26084667
PMCPMC4684598

What OpenQuestion holds

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Registered trials

None linked

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.