A qualitative case study of health system barriers and facilitators to living donor kidney transplantation in Canada's most populous province
Bibliographic record
Abstract
Abstract Patients with kidney failure need dialysis or a kidney transplant to survive. Living donor kidney transplantation (LDKT) is the best therapeutic option, yet global rates of LDKT have minimally increased, and there are disparities in access. The need for a systems approach to improvement has been highlighted. We aimed to understand what elements of a relatively large health system interact to deliver LDKT and act as facilitators or barriers. This was an exploratory case study of Ontario, the most populous province in Canada that conducts 600–700 kidney transplantations annually, of which ~30% are LDKT. Data collection entailed interviews with multiple stakeholders (n = 37), and document review (n = 29) served as a means of triangulating the data. Data were analyzed using inductive thematic analysis. A multicomponent intervention to facilitate kidney transplantation was simultaneously being conducted which allowed us to capture its impact (EnAKT‐LKD). Eight themes were identified, that were separated into facilitators and barriers. Centralized leadership, directed resource deployment, dynamic communities of practice, and informal collaborations between various elements of the health system were found to facilitate LDKT. Barriers were inadequate donor and patient resources to support equitable access, lengthy and poorly coordinated workups for donors and recipients, and issues of jurisdictional control and competition for resources. The EnAKT‐LKD initiative was described as having boosted resource deployment and collaborative capacity and improved strategic alliances by establishing communities of practice. This case study has identified how individual elements in a health system interact to facilitate and impede the delivery of a therapy to patients.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".