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Record W3048778213 · doi:10.1177/2054358120948293

Barriers to Home Hemodialysis Across Saskatchewan, Canada: A Cross-Sectional Survey of In-Center Dialysis Patients

2020· article· en· W3048778213 on OpenAlexaffabout
Lucas Diebel, Maryam Jafari, Sachin Shah, Christine Day, Connie McNaught, Bhanu Prasad

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2020
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsRegina General HospitalSaskatchewan Health AuthoritySt. Paul's HospitalSaskatchewan HealthUniversity of Saskatchewan
FundersBaxter Healthcare Corporation
KeywordsMedicineHome hemodialysisObservational studyCross-sectional studyHemodialysisFamily medicineDialysisPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Background: Despite clinical and lifestyle advantages of home hemodialysis (HHD) compared with in-center hemodialysis (ICHD), it remains underutilized in our province. The aim of the study was to explore the patients’ perception and to identify the barriers to use of HHD in Saskatchewan, Canada. Objectives: The primary objective of the study was to evaluate and explore patient perceptions of HHD and to identify the obstacles for adoption of HHD in Saskatchewan. The secondary objective was to examine variations in the patients’ perceptions and barriers to HHD by center (main dialysis units vs satellite dialysis units). Design: This is a cross-sectional observational survey study. Setting: Two major centers (Regina and Saskatoon) and 5 associated satellite units attached to each center across the province of Saskatchewan. Patients: We approached all prevalent ICHD patients across Saskatchewan, 398 agreed to participate in the study. Measurements: Self-reported barriers to HHD were assessed using a questionnaire. Methods: A questionnaire was designed to determine the patients’ perceived barriers to HHD. Descriptive statistics was used to present the data. Chi-square and Mann-Whitney U test were used to compare the patients’ responses between main and satellite units Results: Satisfaction with current dialysis care (91%), increase in utility bills (65%), fear of catastrophic events at home (59%), medicalization of one’s home (54%), and knowledge deficits toward treatment modalities (54%) were the main barriers to HHD uptake. Compared with patients dialyzing in our main units, satellite patients chose not to pursue HHD more frequently because they had greater satisfaction with their current dialysis unit care (97% vs 87%, P < .001), felt more comfortable dialyzing under the supervision of medical staff (95% vs 86%, P < .007), could not afford additional utility costs (92% vs 45%, P < .001), were unaware of the risks and benefits of HHD (83% vs 33%, P < .001), had concerns over time commitments for training to HHD (69% vs 32%, P < .001), and had concern for family burnout (60.8% vs 40.6%, P < .001). Limitations: We used questionnaires to quantify known barriers, and this prevents inclusion of additional barriers that individual patients may consider important. Cross-sectional data can only be used as a snapshot. Only 398 patients agreed to participate, and the results cannot be generalized to 740 prevalent HD patients. We did not capture data on demographics (age, income, and literacy level), comorbidities, and dialysis vintage, which would have been helpful in interpretation of the results. Conclusions: Satisfaction with in-center care, lack of awareness and education, specifically in the satellite population, concerns with family burnout, expenses associated with utilities, and training time will need to be addressed to increase the uptake of HHD. Trial Registration: The study was not registered on a publicly accessible registry as it did not involve any health care intervention on human participants.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.039
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2020
Admission routes2
Has abstractyes

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