Patient Engagement and Patient-Centred Care in the Management of Advanced Chronic Kidney Disease and Chronic Kidney Failure
Bibliographic record
Abstract
PURPOSE: The purpose of this article is to review the current status of patient-centred care (PCC) and patient engagement (PE) in the management of patients with advanced chronic kidney disease (CKD) and end-stage renal disease (ESRD), to identify some of the barriers that exist to the achievement of PCC and PE, and to describe how these barriers can be overcome. SOURCES OF INFORMATION: The review is based on the professional experience of one of the authors (RB) as a Nephrologist and health care consultant, on the MBA thesis of one of the authors (SS) and on a review of pertinent internet-based information and published literature. FINDINGS: Evidence exists that, currently, the care of patients with advanced CKD and ESRD is not fully patient-centred or fully supportive of PE. A number of barriers exist, including: conflict with other priorities; lack of training and fear of change; the unequal balance of power between patients and providers; physician culture and behaviour; the fee-for-service model of physician compensation; slow implementation of electronic health records; and, fear of accountability. These barriers can be overcome by committed leadership and the development of an information-based implementation plan. Established Renal Agencies in Canada appear interested in facilitating this work by collaborating in the development of a toolkit of recommended educational resources and preferred implementation practices for use by ESRD Programs. LIMITATIONS: A limitation of this review is the absence of a substantial pre-existing literature on this topic. IMPLICATIONS: Receiving care that is patient-centred and that promotes PE benefits patients with serious chronic diseases such as advanced CKD and ESRD. Considerable work is required by ESRD Programs to ensure that such care is provided. Canadian Renal Agencies can play an important role by ensuring that ESRD Programs have access to essential educational material and proven implementation approaches and that implementation successes are celebrated. In this area, enabling policies are required, as are clinical research studies focusing on the measurement of outcomes.
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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".