Paving the way for solutions improving access to kidney transplantation: a qualitative study from a multistakeholder perspective
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to obtain an in-depth perspective from stakeholders involved in access to kidney transplantation to pave the way for solutions in improving access to kidney transplantation. This study qualitatively explored factors influencing optimal access to kidney transplantation from a broad stakeholder perspective. DESIGN: A qualitative study was performed using semistructured interviews both in focus groups and with individual participants. All interviews were recorded, transcribed and coded according to the principles of grounded theory. SETTING: Participants were healthcare providers (geographically spread), patients and (former living) kidney donors, policy-makers and insurers. PARTICIPANTS: Stakeholders (N=87) were interviewed regarding their perceptions, opinions and attitudes regarding access to kidney transplantation. RESULTS: The problems identified by stakeholders within the domains-policy, medical, psychological, social and economic-were acknowledged by all respondents. According to respondents, more efforts should be made to make healthcare providers and patients aware of the clinical guideline for kidney transplantation. The same opinion applied to differences in medical inclusion criteria used in the different transplantation centres. Stakeholders saw room for improvement based on psychological and social themes, especially regarding the provision of information. Many stakeholders described the need to rethink the current economic model to improve access to kidney transplantation. This discussion led to a definition of the most urgent problems for which, according to the respondents, a solution must be sought to optimise access to kidney transplantation. CONCLUSIONS: Stakeholders indicated a high sense of urgency to solve barriers in patient access to kidney transplantation. Moreover, it appears that some barriers are quite straightforward to overcome; according to stakeholders, it is striking that this process has not yet been overcome. Stakeholders involved in kidney transplantation have provided directions for future solutions, and now it is possible to search for solutions with them.
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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".