A Research Protocol for Implementation and Evaluation of a Patient-Focused eHealth Intervention for Chronic Kidney Disease
Bibliographic record
Abstract
Abstract Self-management in chronic kidney disease (CKD) can slow disease progression; however, there are few tools available to support patients with early CKD. My Kidneys My Health is a patient-focused electronic health (eHealth) self-management tool developed by patients and caregivers. This study will investigate the implementation of My Kidneys My Health across primary care and general nephrology clinics. The study aims to: (1) identify and address barriers and facilitators that may impact implementation and sustainability of the website into routine clinical care; (2) evaluate implementation quality to inform spread and scale-up. We will conduct a multi-stage approach using qualitative methods, guided by the Quality Implementation Framework and using a qualitative content analysis approach. First, we will identify perceived barriers and facilitators to implementation and considerations for sustainability through interviews with clinicians, based on the Readiness Thinking Tool and the Long Term Success Tool. Analysis will be guided by the Consolidated Framework for Implementation Research and the Theoretical Domains Framework. Appropriate implementation strategies will be identified using the Expert Recommendations for Implementing Change compilation, and implementation plans will be developed based on Proctor’s recommendations and the Action, Actor, Context, Target, Time framework. Finally, we will explore implementation quality guided by the RE-AIM framework. There is limited literature describing systematic approaches to implementing and sustaining patient-focused self-management tools into clinical care, in addition to employing tailored implementation strategies to promote adoption and sustainability. We aim to generate insights on how My Kidneys My Health can be integrated into clinical care and how to sustain use of patient-centric eHealth tools in clinical settings on a larger scale.
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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 | 0.152 | 0.115 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.105 | 0.023 |
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".