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Record W4415062017 · doi:10.1111/hex.70466

How Are People Undergoing Dialysis Expected to Benefit From Cognitive Behavioural Therapy? A Realist Analysis

2025· article· en· W4415062017 on OpenAlexafffundabout
Katrin Micklitz, Joanne Greenhalgh, Lori Suet Hang Lo, Richard Sawatzky, Kara Schick‐Makaroff

Bibliographic record

VenueHealth Expectations · 2025
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsTrinity Western UniversityProvidence Health Care Research InstituteProvidence Health CareWestern UniversityAlberta Health Services
FundersAlberta InnovatesGöteborgs UniversitetCanada Research Chairs
KeywordsCognitionAdvisory committeeDialysisMEDLINEEthics committeeSocial cognitive theory

Abstract

fetched live from OpenAlex

INTRODUCTION: Depressive symptoms remain inadequately addressed and undertreated in people who receive life-prolonging dialysis treatment. Cognitive behavioural therapy (CBT) has been shown to be effective for treating depression; however, we lack an understanding of how and under what circumstances people with depressive symptoms receiving dialysis may benefit from it. The aim of this study is to identify ideas underlying CBT in general and develop an initial programme theory that explains how these ideas might apply to people receiving dialysis. It is the first step of a theory-driven explanatory realist synthesis and realist evaluation. METHODS: This study included a broad literature search and interviews with seven CBT therapists across Canada and the United States. Search terms were derived from CBT and refined to theory-based literature, literature reviews and book chapters. Therapists were recruited through team collaborators and had experience in developing or providing CBT to adults with depressive symptoms, including those receiving dialysis. Qualitative analysis of data from the literature and interviews focused on identifying mechanisms through which CBT is expected to reduce depressive symptoms in people receiving dialysis and the circumstances that may shape these mechanisms. RESULTS: Based on our findings from 30 documents and the interview data, individuals living with dialysis treatment and experiencing depressive symptoms may benefit from CBT through (1) cognitive changes related to their illness and self; (2) experiencing pleasant emotions; and (3) feeling seen, understood and accepted. However, people's capacity to engage with CBT may be limited due to significant illness and treatment burdens, as well as the perceived stigma of mental health issues. Our findings can be explained by the cognitive behavioural model, illness adjustment theories such as the common-sense model of self-regulation, response shift theory, client-centred therapy, and the cumulative complexity model. CONCLUSION: This study contributes to knowledge by explaining how the illness context of dialysis treatment might shape the mechanisms through which CBT is expected to work. Understanding the dialysis illness context when developing psychosocial interventions such as CBT can advance the provision of person-centred mental health kidney care. PATIENT OR PUBLIC CONTRIBUTION: This patient-oriented research leveraged established partnerships including a Community Advisory Committee, an equity, diversity, inclusivity (EDI) champion, industry partner, kidney administrators and clinicians, and CBT experts. The Community Advisory includes 10 people who have met monthly for over 10 years; the Committee itself is co-chaired by a person with lived experience. The Community Advisors collaborated on the original study idea, participated in grant proposal development, gave feedback on ethics applications and study design, provided input on the initial programme theory, and co-presented at provincial Nephrology Grand Rounds and Research days. They continue to lead in the next phases of this project.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.330
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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