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Record W4389101558 · doi:10.1016/j.xkme.2023.100749

Polypharmacy and Quality of Life Among Dialysis Patients: A Qualitative Study

2023· article· en· W4389101558 on OpenAlexfundno aff
Julia M.T. Colombijn, Freek Colombijn, Lideweij van Berkom, Lia A. van Dijk, Dionne Senders, Charlotte Tierolf, Alferso C Abrahams, Brigit C. van Jaarsveld

Bibliographic record

VenueKidney Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersHartstichtingDutch Cardiovascular AllianceAlberta Conservation Association
KeywordsPolypharmacyThematic analysisQualitative researchMedicineContext (archaeology)Quality of life (healthcare)DialysisDeprescribingNursingPsychiatryIntensive care medicine

Abstract

fetched live from OpenAlex

Rationale & Objective: Almost all patients who receive dialysis experience polypharmacy, but little is known about their experiences with medication or perceptions toward it. In this qualitative study, we aimed to gain insight into dialysis patients' experiences with polypharmacy, the ways they integrate their medication into their daily lives, and the ways it affects their quality of life. Study Design: Qualitative study using semistructured interviews. Setting & Participants: Patients who received dialysis from 2 Dutch university hospitals. Analytical Approach: Interviews were transcribed verbatim and analyzed independently by 2 researchers through thematic content analysis. Results: Overall, 28 individuals were interviewed (29% women, mean age 63 ± 16 years, median dialysis vintage 25.5 [interquartile range, 15-48] months, mean daily number of medications 10 ± 3). Important themes were as follows: (1) their own definition of what constitutes "medication," (2) their perception of medication, (3) medication routines and their impact on daily (quality of) life, and (4) interactions with health care professionals and others regarding medication. Participants generally perceived medication as burdensome but less so than dialysis. Medication was accepted as an essential precondition for their health, although participants did not always notice these health benefits directly. Medication routines and other coping mechanisms helped participants reduce the perceived negative effects of medication. In fact, medication increased freedom for some participants. Participants generally had constructive relationships with their physicians when discussing their medication. Limitations: Results are context dependent and might therefore not apply directly to other contexts. Conclusions: Polypharmacy negatively affected dialysis patients' quality of life, but these effects were overshadowed by the burden of dialysis. The patients' realization that medication is important to their health and effective coping strategies mitigated the negative impact of polypharmacy on their quality of life. Physicians and patients should work together continuously to evaluate the impact of treatments on health and other aspects of patients' daily lives. Plain-Language Summary: People receiving dialysis treatment are prescribed a large number of medications (polypharmacy). Polypharmacy is associated with a number of issues, including a lower health-related quality of life. In this study we interviewed patients who received dialysis treatment to understand how they experience polypharmacy in the context of their daily lives. Participants generally perceived medication as burdensome but less so than dialysis and accepted medication as an essential precondition for their health. Medication routines and other coping mechanisms helped participants mitigate the perceived negative effects of medication. In fact, medication led to increased freedom for some participants. Participants had generally constructive relationships with their physicians when discussing their medication but felt that physicians sometimes do not understand 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.397
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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