Weight Management Experiences Among People With CKD: A Qualitative Study
Bibliographic record
Abstract
Background: Obesity is a chronic disease which directly contributes to the onset and progression of chronic kidney disease (CKD). For patients with advanced CKD (CKD G4-5D), kidney transplantation is the optimal treatment to improve morbidity and quality of life. However, obesity is a barrier to transplantation, due to an associated risk of postoperative complications and decreased graft survival. Objective: We sought to understand patient experiences with weight management and CKD to inform future studies in this area. Design: Descriptive qualitative study. Setting: London, Ontario, Canada. Participants: Individuals with CKD G4-5D and experiences with obesity and weight management. Methods: We interviewed 12 participants with CKD G4-5ND, using thematic analysis and a phenomenological framework. We explored their beliefs, experiences, and expectations of weight loss management. An inductive, open coding technique was used to generate themes that informed our understanding of their shared experiences. Results: We identified 6 themes from our data: strengths and gaps in healthcare support, influence of social circles and systems, past experiences with weight loss, limitations of current health status, knowledge and motivation around weight management, and personal autonomy in treatment choices. Limitations: Small homogenous population limits generalizability, self-report of weight loss attempts without numerical data. Conclusions: Our study emphasizes opportunities for healthcare providers to identify and address potential unmet needs in weight management, while also guiding patient-centered conversations on this topic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".