Barriers to, and Facilitators of, Diabetes Self-management in the Dialysis Population: A Narrative Review and Implications for Research
Bibliographic record
Abstract
Purpose of review: Patients with both diabetes and kidney failure requiring dialysis are a complex population that is at risk of diabetes-related complications, hospitalizations, and mortality. Due to the significant illness burden, self-management of diabetes becomes challenging. The purpose of this review was to identify and synthesize the literature on barriers to, and facilitators of, diabetes self-management among patients with both diabetes and kidney failure requiring dialysis. Sources of information: We conducted a search of health care databases (CINAHL, PubMed, OVID Medline) to find studies that were focused on exploring barriers to, and facilitators of, diabetes self-management in this population. We included English-language qualitative, quantitative, and mixed-methods studies. Methods: We performed a focused narrative review assessing barriers and facilitators to diabetes management among patients with chronic kidney disease. The literature was critically analyzed using various appraisal tools, and thematic analysis was performed. Key findings: A total of 134 articles were identified. Eight articles met inclusion criteria. A review of the articles revealed barriers in diabetes self-management covering 5 themes: financial limitations, limited access to healthcare services, siloed and fragmented care, increased complexity of the dietary regimen, and the higher burden of health. Three themes were revealed pertaining to facilitators of diabetes self-management: self-management support and education, coordinated care between healthcare providers, and family support. Limitations: The literature search was in-depth and comprehensive, but not exhaustive. Also, we restricted our search criteria to articles published in the English language. Implications: There can be challenges living with multiple chronic conditions, especially for those with comorbid diabetes and kidney failure requiring dialysis. This study underscores the urgent need for quality improvement and research initiatives to support these individuals. In addition, conducting further qualitative research to explore the perspectives of dialysis patients, their health care professionals, and caregivers would be beneficial.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".