Protocol for a Process Evaluation of the Quality Improvement Intervention to Enhance Access to Kidney Transplantation and Living Kidney Donation (EnAKT LKD) Cluster-Randomized Clinical Trial
Bibliographic record
Abstract
Background: Many patients who would benefit from a kidney transplant never receive one. The Enhance Access to Kidney Transplantation and Living Kidney Donation (EnAKT LKD) pragmatic, cluster-randomized clinical trial is testing whether a multi-component quality improvement intervention, provided in chronic kidney disease (CKD) programs (vs. usual care), can help patients with CKD with no recorded contraindications to kidney transplant complete more steps toward receiving a transplant (primary outcome of the trial). The EnAKT LKD intervention has 4 components: (1) quality Improvement teams and administrative support, (2) improved transplant education for patients and healthcare providers, (3) access to support and (4) program-level performance monitoring. Objective: To conduct a process evaluation of the EnAKT LKD quality improvement intervention to determine if the components were delivered, received, and enacted as designed (fidelity), and if the intervention addressed intended barriers (mechanisms of change). Design: A mixed-methods process evaluation informed by new practice implementation and theories of behavior change. Setting: Chronic kidney disease programs in Ontario, Canada, began receiving the EnAKT LKD intervention on November 1, 2017 and will continue to receive it until December 31, 2021. The process evaluation (interviews and surveys) will occur alongside the trial, between December 2020 to May 2021. Participants: Healthcare providers (eg, dialysis nurses, nephrologists, members of the multi-care kidney clinic team) at Ontario's 27 CKD programs. Methods: We will survey and interview healthcare providers at each CKD program, and complete an intervention implementation checklist. Quantitative data from the surveys and the intervention implementation checklist will assess fidelity to the intervention, while quantitative and qualitative data from surveys and interviews will provide insight into the mechanisms of change. Limitations: The long trial period may result in poor participant recall. Conclusion: This process evaluation will enhance interpretation of the trial findings, guide improvements in the intervention components, and inform future implementation. Trial registration: Clinicaltrials.gov; identifier: NCT03329521.
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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 | 0.082 | 0.081 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.178 | 0.032 |
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".