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Record W4306247300 · doi:10.1097/md.0000000000030937

Advocating for in-center hemodialysis patients via anonymous survey

2022· article· en· W4306247300 on OpenAlexaff
Arun Rajasekaran, Anand Prakash, Spencer Hatch, Yan Lu, Gary Cutter, Abolfazl Zarjou

Bibliographic record

VenueMedicine · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsHatch (Canada)
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineLikert scaleHemodialysisPatient satisfactionFamily medicineRating scaleHealth careScale (ratio)NursingPsychiatry

Abstract

fetched live from OpenAlex

We conducted an anonymous survey in 9 of our university affiliated outpatient dialysis units to address the concern that many in-center hemodialysis patients may not feel comfortable sharing their experiences. Major goals of this study: Investigating level of patient satisfaction with their care; Evaluating the subjective perception of the level of understanding of patients regarding pertinent issues of their disease and its management; Identifying potential avenues for care improvement. Survey was conducted in English, paper-based, with answer choices to individual questions for patient satisfaction and education graded using a 5-point Likert scale. Regarding potential areas of improvement, patients were asked to choose as many areas as deemed appropriate. To ensure anonymity, the completed surveys were folded and dropped into a box. Overall, 253 out of 516 (49%) screened patients were eligible and completed the survey. Patients expressed favorable responses regarding satisfaction (mean rating > 4 in each of 14 questions) and education (mean rating > 4 in 8 questions, > 3.5 in 2 questions) regarding hemodialysis. About 62% of overall study participants identified at least one area where they felt additional information would result in improvement of care. Our results indicate that patients undergoing outpatient hemodialysis were overall satisfied and had a good perceptive understanding about their health. Based on the patients' input, strategies focused on addressing pain and discomfort, privacy, providing information about palliative care/hospice, mental health resources, and the process of kidney transplantation may promote improvement in overall quality of care.

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)0.006
metaresearch head score (Gemma)0.014
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 score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.114
GPT teacher head0.445
Teacher spread0.331 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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