CoChat
Sign Up
Cited by 40Gambling Behavior and TreatmentsSubstance Abuse Treatment and OutcomesAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

Towards a better understanding of gambling treatment failure: Implications of translational research

Daughters, Stacey B., Lejuez, C.W., Lesieur, Henry R., Strong, David R., Zvolensky, Michael J.·2003·Clinical Psychology Review
DOI 10.1016/s0272-7358(03)00037-0
Open this paper in CoChat — free account

Abstract

Despite a recent proliferation of gambling treatment protocols and studies examining the success of these protocols, there exists an absence of attention to mechanisms of change and the individual characteristics that predict treatment outcome. A better understanding of the characteristics that place particular individuals at-risk for treatment failure will be vital for developing new treatments as well as modifying existing treatments. Furthermore, the development of tools to identify those at risk for treatment failure, prior to treatment initiation, will allow for the tailoring of protocols to suit individual needs and vulnerabilities. In the current paper, we review the state of gambling treatment and suggest a framework for better understanding and ultimately reducing gambling treatment failure.

Integrity signals

Registry

Crossref · 10.1016/s0272-7358(03)00037-0

Cites

75 of 75 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Identification of client characteristics predictive of successful treatment outcomes can potentially improve efficacy by tailoring interventions to meet individual needs.

    “The identification of client characteristics that are predictive of successful treatment outcomes can potentially improve the efficacy of treatment by tailoring interventions to meet individual needs (Adamson, Sellman, & Frampton, 2009;Barnicot et al., 2012;Daughters, Lejuez, Lesieur, Strong, & Zvolensky, 2003).”

    From Predictors of outcomes of psychological treatments for disordered gambling: A systematic review (2016) · cites this paper as Daughters et al., 2003

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Daughters, S. B., Lejuez, C. W., Lesieur, H. R., Strong, D. R., & Zvolensky, M. J.
Affiliations
  • University of Maryland, College Park (US)
  • Rhode Island Hospital (US)
  • Butler Hospital (US)
  • University of Vermont (US)
Corresponding author
Lejuez, C.W.
ORCID
Venue
Clinical Psychology Review
Publisher
Elsevier BV
Published
2003-07
Volume
23
Issue
4
Pages
573-586
ISSN
0272-7358 (ISSN-L)
DOI
10.1016/s0272-7358(03)00037-0
OpenAlex
W2015929240
Cited by
40
References
70
Citations by year
2024: 2 · 2022: 1 · 2019: 2 · 2018: 1 · 2017: 1 · 2016: 3 · 2015: 2 · 2014: 4 · 2013: 5 · 2012: 1
FWCI
2.04
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Clinical Psychology Review (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Psychology › Clinical Psychology › Gambling Behavior and Treatments
  • Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Keywords
gambling addiction, mechanisms of change, predictors of treatment outcome, translational research, personalized treatment, treatment tailoring, at-risk individuals, treatment protocols
MeSH terms
Clinical Trials as Topic, Gambling, Humans, Patient Care Planning, Prognosis, Risk Factors, Treatment Failure

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review