Health related quality of life during dialysis modality transitions: a qualitative study
Bibliographic record
Abstract
Abstract Background Modality transitions represent a period of significant change that can impact health related quality of life (HRQoL). We explored the HRQoL of adults transitioning to new or different dialysis modalities. Methods We recruited eligible adults (≥ 18) transitioning to dialysis from pre-dialysis or undertaking a dialysis modality change between July and September 2017. Nineteen participants (9 incident and 10 prevalent dialysis patients) completed the KDQOL-36 survey at time of transition and three months later. Fifteen participants undertook a semi-structured interview at three months. Qualitative data were thematically analyzed. Results Four themes and five sub-themes were identified: adapting to new circumstances (tackling change, accepting change), adjusting together, trading off, and challenges of chronicity (the impact of dialysis, living with a complex disease, planning with uncertainty). From the first day of dialysis treatment to the third month on a new dialysis therapy, all five HRQoL domains from the KDQOL-36 (symptoms, effects, burden, overall PCS, and overall MCS) improved in our sample (i.e., those who remained on the modality). Conclusions Dialysis transitions negatively impact the HRQoL of people with kidney disease in various ways. Future work should focus on how to best support people during this time.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".