Understanding health decisions using critical realism: home‐dialysis decision‐making during chronic kidney disease
Bibliographic record
Abstract
Understanding health decisions using critical realism: home-dialysis decision-making during chronic kidney disease This paper examines home-dialysis decision making in people with Chronic Kidney Disease (CKD) from the perspective of critical realism. CKD programmes focus on patient education for self-management to delay the progression of kidney disease and the preparation and support for renal replacement therapy e.g.) dialysis and transplantation. Home-dialysis has clear health, societal and economic benefits yet service usage is low despite efforts to realign resources and educate individuals. Current research on the determinants of modality selection is superficial and insufficient to capture the complexities embedded in the process of dialysis modality selection. Predictors of home-dialysis selection and the effect of chronic kidney disease educational programmes provide a limited explanation of this experience. A re-conceptualization of the problem is required in order to fully understand this process. The epistemology and ontology of critical realism guides our knowledge and methodology particularly suited for examination of these complexities. This approach examines the deeper mechanisms and wider determinants associated with modality decision making, specifically who chooses home dialysis and under what circumstances. Until more is known regarding dialysis modality decision making service usage of home dialysis will remain low as interventions will be based on inadequate epistemology.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".