Health System–Level Barriers to Living Donor Kidney Transplantation: Protocol for a Comparative Case Study Analysis
Bibliographic record
Abstract
BACKGROUND: Living donor kidney transplantation (LDKT) is the best treatment option for patients with kidney failure and offers significant medical and economic advantages for both patients and health systems. Despite this, rates of LDKT in Canada have stagnated and vary significantly across Canadian provinces, the reasons for which are not well understood. Our prior work has suggested that system-level factors may be contributing to these differences. Identifying these factors can help inform system-level interventions to increase LDKT. OBJECTIVE: Our objective is to generate a systemic interpretation of LDKT delivery across provincial health systems with variable performance. We aim to identify the attributes and processes that facilitate the delivery of LDKT to patients, and those that create barriers and compare these across systems with variable performance. These objectives are contextualized within our broader goal of increasing rates of LDKT in Canada, particularly in lower-performing provinces. METHODS: This research takes the form of a qualitative comparative case study analysis of 3 provincial health systems in Canada that have high, moderate, and low rates of LDKT performance (the percentage of LDKT to all kidney transplantations performed). Our approach is underpinned by an understanding of health systems as complex adaptive systems that are multilevel and interconnected, and involve nonlinear interactions between people and organizations, operating within a loosely bounded network. Data collection will comprise semistructured interviews, document reviews, and focus groups. Individual case studies will be conducted and analyzed using inductive thematic analysis. Following this, our comparative analysis will operationalize resource-based theory to compare case study data and generate explanations for our research question. RESULTS: This project was funded from 2020 to 2023. Individual case studies were carried out between November 2020 and August 2022. The comparative case analysis will begin in December 2022 and is expected to conclude in April 2023. Submission of the publication is projected for June 2023. CONCLUSIONS: By investigating health systems as complex adaptive systems and making comparisons across provinces, this study will identify how health systems can improve the delivery of LDKT to patients with kidney failure. Our resource-based theory framework will provide a granular analysis of the attributes and processes that facilitate or create barriers to LDKT delivery across multiple organizations and levels of practice. Our findings will have practice and policy implications and help inform transferrable competencies and system-level interventions conducive to increasing LDKT. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44172.
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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 | 0.102 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.097 | 0.010 |
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".