Living Donor Kidney Transplantation in Quebec: A Qualitative Case Study of Health System Barriers and Facilitators
Bibliographic record
Abstract
Background:Patients with kidney failure represent a major public health burden, and living donor kidney transplantation (LDKT) is the best treatment option for these patients. Current work to optimize LDKT delivery to patients has focused on microlevel interventions and has not addressed interdependencies with meso and macro levels of practice.Objective:We aimed to learn from a health system with historically low LDKT performance to identify facilitators and barriers to LDKT. Our specific aims were to understand how LDKT delivery is organized through interacting macro, meso, and micro levels of practice and identify what attributes and processes of this health system facilitate the delivery of LDKT to patients with kidney failure and what creates barriers.Design:We conducted a qualitative case study, applying a complex adaptive systems approach to LDKT delivery, that recognizes health systems as being made up of dynamic, nested, and interconnected levels, with the patient at its core.Setting:The setting for this case study was the province of Quebec, Canada.Participants:Thirty-two key stakeholders from all levels of the health system. This included health care professionals, leaders in LDKT governance, living kidney donors, and kidney recipients.Methods:Semi-structured interviews with 32 key stakeholders and a document review were undertaken between February 2021 and December 2021. Inductive thematic analysis was used to generate themes.Results:Overall, we identified strong links between system attributes and processes and LDKT delivery, and more barriers than facilitators were discerned. Barriers that undermined access to LDKT included fragmented LDKT governance and expertise, disconnected care practices, limited resources, and regional inequities. Some were mitigated to an extent by the intervention of a program launched in 2018 to increase LDKT. Facilitators driven by the program included advocacy for LDKT from individual member(s) of the care team, dedicated resources, increased collaboration, and training opportunities that targeted LDKT delivery at multiple levels of practice.Limitations:Delineating the borders of a “case” is a challenge in case study research, and it is possible that some perspectives may have been missed. Participants may have produced socially desirable answers.Conclusions:Our study systematically investigated real-world practices as they operate throughout a health system. This novel approach has cross-disciplinary methodological relevance, and our findings have policy implications that can help inform multilevel interventions to improve LDKT.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".