Patient Perspectives of Center-Specific Reporting in Kidney Failure Care: An Australian Qualitative Study
Bibliographic record
Abstract
Introduction: Public reporting of quality of care indicators in healthcare is intended to inform consumer decision-making; however, people may be unaware that such information exists, or it may not capture their priorities. The aim of this study was to understand the views of people with kidney disease about public reporting of dialysis and transplant center outcomes. Methods: This qualitative study involved 27 patients with lived experience of kidney disease in Australia who participated in 11 online focus groups between August and December 2022. Transcripts were analyzed thematically. Results: Patients from all Australian states and territories participated, with 22 (81%) having a functioning kidney transplant and 22 (81%) having current or previous experience of dialysis. Five themes were identified as follows: (i) surrendering to the health system, (ii) the complexity of quality, (iii) benefits for patient care and experience, (iv) concerned about risks and unintended consequences, and (v) optimizing the impact of data. Conclusion: Patients desire choice among kidney services but perceive this as rarely possible in the Australian context. Health professionals are trusted to make decisions about appropriate centers. Public reporting of center outcomes may induce fear and a loss of balanced perspective; however, it was supported by all participants and represents an opportunity for self-advocacy and informed decision-making. Strategies to mitigate potential risks include availability of trusted clinicians and community members to aid in data interpretation, providing context about centers and patients, and framing statistics to promote positivity and hope.
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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".