Systematic Review of Patient and Caregiver Involvement in CKD Research
Bibliographic record
Abstract
Introduction: Limited consumer involvement in chronic kidney disease (CKD) research may reduce its relevance, impact, and transferability into practice and policy. We aimed to describe the current landscape of consumer (patients with CKD and caregivers) involvement in published CKD research. Methods: Electronic databases were searched to August 2023. Articles describing consumer involvement in CKD research were eligible. All text were imported into NVivo for line-by-line coding using descriptive synthesis of these domains: defining involvement, purpose of involvement, selection, stages of the research, resources, and evaluation. Results: = 24, 22%). Barriers included limited resources (i.e., financial, logistical, or training) and the need for tailored solutions continue to exist. Consumer involvement resulted in increased recruitment and retention, richer data, and more useful outputs for end users. Conclusions: Consumers were mostly involved in discrete activities with limited decision-making power. Increasing financial, logistical, and training resources for consumers may support more meaningful involvement. Ongoing evaluation of processes or impacts of consumer involvement, including consistent reporting, is needed to strengthen evidence and practice in CKD research.
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 | 0.049 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".