Issues with deprescribing in haemodialysis: a qualitative study of patient and provider experiences
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Patients undergoing haemodialysis receive on average 10-17 medications, which increase the risk of falls, adverse drug reactions and hospitalizations. Supervised discontinuation of potentially inappropriate medications may lower these risks. Although many calls have been made for deprescribing in the haemodialysis setting, little is known about how patients and providers in this setting experience it. The aim of this study is to explore patient and provider experiences and perceptions of one of the rare deprescribing intervention in haemodialysis. METHODS: Ten semi-structured interviews were held with patients, and a focus group was done with dialysis clinic team members at a Montreal area health network's haemodialysis clinic after the implementation of a standardized deprescribing intervention using the patient-as-partner approach. The interviews and focus group were recorded, and verbatims were coded to determine emerging themes. Grounded theory was used for interview guide design and data analysis. RESULTS: The three emerging themes were (1) ambivalence towards medication creating a favourable context for deprescribing, (2) the empowering elements of the deprescribing process and (3) the uncertain future of deprescribing in the clinics even though the intervention was considered successful. CONCLUSION: Haemodialysis patients and providers viewed deprescribing favourably, believed the intervention was valuable, and offered suggestions for long term implementation while expressing concerns about feasibility. Notwithstanding the underlying uncertainties, a structured and integrated approach in routine practice involving all members of the care team may facilitate the continuity of deprescribing as an intervention in the setting of a haemodialysis clinic.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".