In‐center hemodialysis attendance: Patient perceptions of risks, barriers, and recommendations
Bibliographic record
Abstract
Missed hemodialysis treatments lead to increased morbidity and mortality in the end-stage renal disease population. Little is known about why patients have difficulty attending their scheduled in-center dialysis treatments. Semistructured interviews with 15 adherent and 15 nonadherent hemodialysis patients were conducted to determine patients' attitudes about dialysis, health beliefs and risk perception regarding missed treatments, barriers and facilitators to hemodialysis attendance, and recommendations to improve the system to facilitate dialysis attendance. Average time on dialysis was 2.5 years for the nonadherent group and 7.3 years in the adherent group. In both groups, patients felt that dialysis is life-saving and a necessity. A substantial number of patients in both groups understood that missing hemodialysis treatments is dangerous and several patients could clearly communicate the risk of skipping. The most common barriers to hemodialysis were inadequate or unreliable transportation (mentioned in both groups) and a lack of motivation to get to dialysis or that dialysis is not a priority (typically mentioned by the nonadherent group). Facilitators to hemodialysis attendance included explanations from the health care team regarding the risk of skipping and relationships with other dialysis patients. Patient recommendations to improve dialysis attendance included continued education about the risk of poor attendance and more accessible transportation. Patients did not feel that home dialysis would improve adherence. Hemodialysis patients must adhere to a complex and burdensome regimen. Through the elucidation of barriers and facilitators to hemodialysis attendance and through specific patient recommendations, at least three interventions may be further investigated to improve hemodialysis attendance: Improvement of the transportation system, education and supportive encouragement from the health care team, and peer support mentorship.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".