University of Hertfordshire

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  • Guillem Feixas
  • A. Bados
  • E. Garcia-Grau
  • A. Montesano
  • G. Dada
  • V. Compañ
  • M. Aguilera
  • M. Salla
  • J. Soldevilla
  • A. Trujillo
  • C. Paz
  • L. Botella
  • S. Corbella
  • L. Saúl-Gutiérrez
  • J. Cañete
  • M. Gasol
  • M. Ibarra
  • L. Medeiros-Ferreira
  • J. Soriano
  • E. Ribeiro
  • F. Caspar
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Original languageEnglish
Article number144
Number of pages11
Publication statusPublished - 2013


Depression is one of the more severe and serious health problems because of its morbidity, disabling effects and for its societal and economic burden. Despite the variety of existing pharmacological and psychological treatments, most of the cases evolve with only partial remission, relapse and recurrence.
Cognitive models have contributed significantly to the understanding of unipolar depression and its psychological treatment. However, success is only partial and many authors affirm the need to improve those models and also the
treatment programs derived from them. One of the issues that requires further elaboration is the difficulty these patients experience in responding to treatment and in maintaining therapeutic gains across time without relapse or
recurrence. Our research group has been working on the notion of cognitive conflict viewed as personal dilemmas according to personal construct theory. We use a novel method for identifying those conflicts using the repertory grid
technique (RGT). Preliminary results with depressive patients show that about 90% of them have one or more of those conflicts. This fact might explain the blockage and the difficult progress of these patients, especially the more severe
and/or chronic. These results justify the need for specific interventions focused on the resolution of these internal conflicts. This study aims to empirically test the hypothesis that an intervention focused on the dilemma(s) specifically
detected for each patient will enhance the efficacy of cognitive behavioral therapy (CBT) for depression


This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

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