The Hopelessness Theory of Depression: Clinical Utility and Generalizability View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-08

AUTHORS

Gerald J. Haeffel, Rachel Hershenberg, Jason T. Goodson, Sascha Hein, Amanda Square, Elena L. Grigorenko, John Chapman

ABSTRACT

To date, “basic” research has dominated the empirical literature on hopelessness theory. The next logical step in this area of research is to determine if the theory can be used to help people. We conducted three studies to determine if the cognitive vulnerability factor featured in hopelessness theory could be reliably measured in diverse samples in a treatment context and if it could predict depressive therapeutic outcomes. Study 1 used a sample of male juvenile detainees (n = 296; 70% from underrepresented groups) and found that cognitive vulnerability moderated the effectiveness of a social problem solving training intervention. Study 2 used a clinical sample of U.S. Veterans (n = 16; 56% from underrepresented groups) enrolled in a cognitive behavioral therapy group for depression and found that cognitive vulnerability predicted post-therapy depressive outcomes. In both Study 1 and Study 2, higher levels of cognitive vulnerability resulted in poorer treatment outcomes (i.e., greater post-treatment levels of depressive symptoms). Study 3 used a clinical sample of U.S. Veterans (n = 76; 67% from underrepresented groups) enrolled in a behavior activation group and found no effect of cognitive vulnerability on post-therapy depressive outcomes. The results of the three studies indicate that hopelessness theory’s cognitive vulnerability construct can be reliably measured in diverse samples in real world clinical contexts and that it has the potential to be a useful predictor of clinical outcomes in the context of cognitively focused treatments. More... »

PAGES

543-555

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10608-017-9833-1

DOI

http://dx.doi.org/10.1007/s10608-017-9833-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084025804


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10608-017-9833-1'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10608-017-9833-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10608-017-9833-1'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10608-017-9833-1'


 

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