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Record W4389990186 · doi:10.1016/j.xkme.2023.100785

Decisional Regret Surrounding Dialysis Initiation: A Comparative Analysis

2023· article· en· W4389990186 on OpenAlexaff
Aditya S. Pawar, Bjorg Thorsteinsdottir, Sam Whitman, Katherine Pine, Alexander Lee, Nataly R. Espinoza Suárez, Anjali Thota, Elizabeth C. Lorenz, Annika T. Beck, Robert C. Albright, Molly A. Feely, Amy W. Williams, Emma Behnken, Kasey R. Boehmer

Bibliographic record

VenueKidney Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleCentres Intégré Universitaires de Santé et de Services SociauxCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthNational Center for Advancing Translational SciencesMayo Clinic
KeywordsRegretDialysisIntrusivenessPsychologyScale (ratio)BlameQualitative researchHemodialysisMedicineSocial psychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Rationale & Objective Dialysis comes with a substantial treatment burden, so patients must select care plans that align with their preferences. We aimed to deepen the understanding of decisional regret with dialysis choices. Study Design This study had a mixed-methods explanatory sequential design. Setting & Participants All patients from a single academic medical center prescribed maintenance in-center hemodialysis or presenting for home hemodialysis or peritoneal dialysis check-up during 3 weeks were approached for survey. A total of 78 patients agreed to participate. Patients with the highest (15 patients) and lowest decisional regret (20 patients) were invited to semistructured interviews. Predictors Decisional regret scale and illness intrusiveness scale were used in this study. Analytical Approach Quantitatively, we examined correlations between the decision regret scale and illness intrusiveness scale and sorted patients into the highest and lowest decision regret scale quartiles for further interviews; then, we compared patient characteristics between those that consented to interview in high and low decisional regret. Qualitatively, we used an adapted grounded theory approach to examine differences between interviewed patients with high and low decisional regret. Results Of patients invited to participate in the interviews, 21 patients (8 high regret, 13 low regret) agreed. We observed that patients with high decisional regret displayed resignation toward dialysis, disruption of their sense of self and social roles, and self-blame, whereas patients with low decisional regret demonstrated positivity, integration of dialysis into their identity, and self-compassion. Limitations Patients with the highest levels of decisional regret may have already withdrawn from dialysis. Patients could complete interviews in any location (eg, home, dialysis unit, and clinical office), which may have influenced patient disclosure. Conclusions Although all patients experienced disruption after dialysis initiation, patients' approach to adversity differs between patients experiencing high versus low regret. This study identifies emotional responses to dialysis that may be modifiable through patient-support interventions. Plain-Language Summary As part of a quality improvement initiative in our dialysis practice, a patient stated, "I wish I never started dialysis." This quote served as the catalyst for embarking on a research project with the aim to understand why patients living with end-stage kidney disease have regret about starting and continuing dialysis, a lifesaving but time-intensive measure. We surveyed and interviewed patients on the topic and learned that patients experiencing regret had a disrupted sense of self and blamed themselves for their need of dialysis. Patients with little to no regret demonstrated positivity and self-compassion. These findings will help health care professionals as they work with patients considering dialysis or having newly started dialysis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.079
GPT teacher head0.369
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2023
Admission routes1
Has abstractyes

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