Factores asociados a la adherencia al tratamiento en personas en hemodiálisis
Bibliographic record
Abstract
Introduction: Treatment adherence in haemodialysis patients is a crucial aspect for improving clinical outcomes and quality of life. Objectives: To identify the factors associated with treatment adherence in people on haemodialysis. Material and Method: A scoping review was conducted. The search protocol was developed in the SciElo, PubMed, Scopus, ScienceDirect, EBSCO, and Cinahl databases, using the Boolean operators "AND," "OR," "NOT," relating the DeCS-MeSH search terms: "Treatment Adherence and Compliance" AND "Kidney Diseases" AND "Renal Dialysis."Results: The review identified 36 articles, most from 2014, with 6 developed in Spain, 5 in Brazil and Iran, 4 in the United States, 3 in Indonesia and China, 2 in Australia and India, 1 in Colombia, Korea, Canada, Mexico, United Kingdom, and Turkey. Written in English, Spanish, and Portuguese. Adherence is influenced by biopsychosocial, economic, demographic, clinical, and psychological factors. Studies highlight the importance of education, economic stability, family relationships, mental health, medical team support, and educational strategies. Conclusions: The complexity of treatment adherence in haemodialysis patients and the need for a comprehensive approach addressing multiple aspects are revealed. The importance of personalised educational programmes, socioeconomic support, effective communication with healthcare professionals, and personalised strategies to improve adherence is emphasised. These findings have important implications for designing interventions that improve quality of life and clinical outcomes in this population
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 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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".