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Record W6945101557 · doi:10.25384/sage.c.6434732.v1

The Patient-Provider Gap: A Cross-sectional Survey to Understand Barriers and Motivating Factors for Home Blood Pressure Monitoring in a CKD Cohort

2023· other· en· W6945101557 on OpenAlexaffabout

Bibliographic record

VenueSage Journals Data · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsProvincial Health Services AuthorityUniversity of British Columbia
Fundersnot available
KeywordsCohortKidney diseaseIntervention (counseling)Best practiceBlood pressureHealth careMEDLINEFocus groupTelemedicine

Abstract

fetched live from OpenAlex

Background:Blood pressure (BP) management can decrease morbidity and mortality in chronic kidney disease (CKD) patients. Evidence-based hypertension guidelines endorse home BP monitoring (HBPM), and the growing use of virtual health has highlighted the need for HBPM. A comprehensive understanding of HBPM adoption in our province is lacking.Objective:To identify the baseline practices, perspectives, barriers, and enablers in both providers and patients in our kidney care clinics regarding HBPM. Ultimately, this will inform the development of a provincial intervention that empowers providers to both increase patient understanding and equip them for accurate and reliable home BP measurement.Design:Cross-sectional, descriptive study using online survey methodology.Setting:Kidney care clinic network in the province of British Columbia, Canada.Patients or Sample or Participants:Kidney care clinic staff and patients who perform HBPM.Methods:Data were collected using semi-structured online surveys, one for staff and one for patients and/or caregivers. These surveys were developed by an interdisciplinary working group that included patient partners and addressed some key components of the implementation of an HBPM program (including perceived barriers to uptake, education, and adoption of best practices).Results:In all, 46 patients and 43 staff responded to the survey from 16 kidney care clinics. Of the patients 53% were women, and the most common age range was 60 to 69 years (25%); 93% of the staff respondents were women and 63% were nurses. We identified numerous areas of discordance between providers and patients and the need for improvement from the perspective of implementing best practices from hypertension guidelines, both in staff teaching and patient usage of HBPM. Blood pressure targets were not known to 18% of patients and 39% of patients had received a BP target from their kidney care clinic team; 89% of patients had not had their upper arm circumference measured for cuff size. Furthermore, 54% of patients knew what to do when their BP is off-target. All recognized the benefits of HBPM, providers were more likely to perceive anxiety as a barrier relative to patients, and patients were more likely to report expense as a barrier than providers.Limitations:This study includes only a single provincial health care system limiting generalizability to other jurisdictions and sampled a small subset of patients and providers.Conclusions:The systematic evaluation of education, understanding, implementation of best practices, and barriers and motivating factors for HBPM from both patient and clinician perspectives is an important step in designing strategies to improve the use of HBPM. Given differences in staff and patient perspectives, targeted interventions based on these responses may lead to improved use of HBPM, and ultimately enhance hypertension self-management and BP control in our CKD patients.

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)0.002
metaresearch head score (Gemma)0.005
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: Dataset · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.124
GPT teacher head0.358
Teacher spread0.234 · 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
GenreDataset

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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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Same venueSage Journals DataFrench-language works237,207